Deploy and monitor a machine learning workflow for Image Classification¶

Setting up this notebook¶

Notes about the instance size and kernel setup: this notebook has been tested on

  1. The Python 3 (Data Science) kernel
  2. The ml.t3.medium Sagemaker notebook instance

Data Staging¶

We'll use a sample dataset called CIFAR to simulate the challenges Scones Unlimited are facing in Image Classification. In order to start working with CIFAR we'll need to:

  1. Extract the data from a hosting service
  2. Transform it into a usable shape and format
  3. Load it into a production system

In other words, we're going to do some simple ETL!

1. Extract the data from the hosting service¶

In the cell below, define a function extract_cifar_data that extracts python version of the CIFAR-100 dataset. The CIFAR dataaset is open source and generously hosted by the University of Toronto at: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz

In [20]:
import requests

def extract_cifar_data(url, filename="cifar.tar.gz"):
    """A function for extracting the CIFAR-100 dataset and storing it as a gzipped file
    
    Arguments:
    url      -- the URL where the dataset is hosted
    filename -- the full path where the dataset will be written
    
    """
    
    # Todo: request the data from the data url
    # Hint: use `requests.get` method
    r = requests.get(url)
    with open(filename, "wb") as file_context:
        file_context.write(r.content)
    return

Let's test it out! Run the following cell and check whether a new file cifar.tar.gz is created in the file explorer.

In [21]:
extract_cifar_data("https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz")

2. Transform the data into a usable shape and format¶

Clearly, distributing the data as a gzipped archive makes sense for the hosting service! It saves on bandwidth, storage, and it's a widely-used archive format. In fact, it's so widely used that the Python community ships a utility for working with them, tarfile, as part of its Standard Library. Execute the following cell to decompress your extracted dataset:

In [22]:
import tarfile

with tarfile.open("cifar.tar.gz", "r:gz") as tar:
    tar.extractall()

A new folder cifar-100-python should be created, containing meta, test, and train files. These files are pickles and the CIFAR homepage provides a simple script that can be used to load them. We've adapted the script below for you to run:

In [23]:
import pickle

with open("./cifar-100-python/meta", "rb") as f:
    dataset_meta = pickle.load(f, encoding='bytes')

with open("./cifar-100-python/test", "rb") as f:
    dataset_test = pickle.load(f, encoding='bytes')

with open("./cifar-100-python/train", "rb") as f:
    dataset_train = pickle.load(f, encoding='bytes')
In [24]:
# Feel free to explore the datasets

dataset_train.keys()
Out[24]:
dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])

As documented on the homepage, b'data' contains rows of 3073 unsigned integers, representing three channels (red, green, and blue) for one 32x32 pixel image per row.

In [25]:
32*32*3
Out[25]:
3072

For a simple gut-check, let's transform one of our images. Each 1024 items in a row is a channel (red, green, then blue). Each 32 items in the channel are a row in the 32x32 image. Using python, we can stack these channels into a 32x32x3 array, and save it as a PNG file:

In [26]:
import numpy as np

# Each 1024 in a row is a channel (red, green, then blue)
row = dataset_train[b'data'][0]
red, green, blue = row[0:1024], row[1024:2048], row[2048:]

# Each 32 items in the channel are a row in the 32x32 image
red = red.reshape(32,32)
green = green.reshape(32,32)
blue = blue.reshape(32,32)

# Combine the channels into a 32x32x3 image!
combined = np.dstack((red,green,blue))

For a more concise version, consider the following:

In [27]:
# All in one:
test_image = np.dstack((
    row[0:1024].reshape(32,32),
    row[1024:2048].reshape(32,32),
    row[2048:].reshape(32,32)
))
In [28]:
import matplotlib.pyplot as plt
plt.imshow(test_image);

Looks like a cow! Let's check the label. dataset_meta contains label names in order, and dataset_train has a list of labels for each row.

In [29]:
dataset_train[b'fine_labels'][0]
Out[29]:
19

Our image has a label of 19, so let's see what the 19th item is in the list of label names.

In [30]:
print(dataset_meta[b'fine_label_names'][19])

b'cattle'

Ok! 'cattle' sounds about right. By the way, using the previous two lines we can do:

In [31]:
n = 0
print(dataset_meta[b'fine_label_names'][dataset_train[b'fine_labels'][n]])

b'cattle'

Now we know how to check labels, is there a way that we can also check file names? dataset_train also contains a b'filenames' key. Let's see what we have here:

In [32]:
print(dataset_train[b'filenames'][0])

b'bos_taurus_s_000507.png'

"Taurus" is the name of a subspecies of cattle, so this looks like a pretty reasonable filename. To save an image we can also do:

In [33]:
plt.imsave("file.png", test_image)

Your new PNG file should now appear in the file explorer -- go ahead and pop it open to see!

Now that you know how to reshape the images, save them as files, and capture their filenames and labels, let's just capture all the bicycles and motorcycles and save them. Scones Unlimited can use a model that tells these apart to route delivery drivers automatically.

In the following cell, identify the label numbers for Bicycles and Motorcycles:

In [34]:
import pandas as pd

# Todo: Filter the dataset_train and dataset_meta objects to find the label numbers for Bicycle and Motorcycles
# Decode byte strings to regular strings for label names
label_names = [label.decode('utf-8') for label in dataset_meta[b'fine_label_names']]

# Find the label numbers for 'Bicycles' and 'Motorcycles'
bicycle_label = label_names.index('bicycle')
motorcycle_label = label_names.index('motorcycle')

print("Label number for 'Bicycles':", bicycle_label)
print("Label number for 'Motorcycles':", motorcycle_label)

Label number for 'Bicycles': 8
Label number for 'Motorcycles': 48

Good job! We only need objects with label 8 and 48 -- this drastically simplifies our handling of the data! Below we construct a dataframe for you, and you can safely drop the rows that don't contain observations about bicycles and motorcycles. Fill in the missing lines below to drop all other rows:

In [35]:
#Construct the dataframe
df_train = pd.DataFrame({
    "filenames": dataset_train[b'filenames'],
    "labels": dataset_train[b'fine_labels'],
    "row": range(len(dataset_train[b'filenames']))
})

# Drop all rows from df_train where label is not 8 or 48
df_train = df_train[(df_train['labels'] == 8) | (df_train['labels'] == 48)]
#TODO: Fill in

# Decode df_train.filenames so they are regular strings
df_train["filenames"] = df_train["filenames"].apply(
    lambda x: x.decode("utf-8")
)


df_test = pd.DataFrame({
    "filenames": dataset_test[b'filenames'],
    "labels": dataset_test[b'fine_labels'],
    "row": range(len(dataset_test[b'filenames']))
})

# Drop all rows from df_test where label is not 8 or 48
df_test = df_test[(df_test['labels'] == 8) | (df_test['labels'] == 48)] #TODO: Fill in

# Decode df_test.filenames so they are regular strings
df_test["filenames"] = df_test["filenames"].apply(
    lambda x: x.decode("utf-8")
)
In [36]:
df_train.head(10)
Out[36]:
filenames labels row
16 bike_s_000682.png 8 16
30 bike_s_000127.png 8 30
130 cycle_s_002598.png 8 130
152 minibike_s_000824.png 48 152
195 motorcycle_s_001856.png 48 195
219 bicycle_s_000537.png 8 219
251 moped_s_000169.png 48 251
252 motorcycle_s_001453.png 48 252
298 safety_bike_s_000482.png 8 298
370 dirt_bike_s_000124.png 48 370
In [37]:
df_test.head(10)
Out[37]:
filenames labels row
27 safety_bike_s_000390.png 8 27
28 bike_s_000658.png 8 28
116 velocipede_s_001744.png 8 116
161 bike_s_000643.png 8 161
319 ordinary_bicycle_s_000437.png 8 319
354 motorcycle_s_000323.png 48 354
417 safety_bike_s_001253.png 8 417
476 cycle_s_001306.png 8 476
532 velocipede_s_000863.png 8 532
554 bicycle_s_000479.png 8 554

Now that the data is filtered for just our classes, we can save all our images.

In [38]:
!mkdir ./train
!mkdir ./test

mkdir: cannot create directory ‘./train’: File exists
mkdir: cannot create directory ‘./test’: File exists

In the previous sections we introduced you to several key snippets of code:

  1. Grabbing the image data:
dataset_train[b'data'][0]
  1. A simple idiom for stacking the image data into the right shape
import numpy as np
np.dstack((
    row[0:1024].reshape(32,32),
    row[1024:2048].reshape(32,32),
    row[2048:].reshape(32,32)
))
  1. A simple matplotlib utility for saving images
plt.imsave(path+row['filenames'], target)

Compose these together into a function that saves all the images into the ./test and ./train directories. Use the comments in the body of the save_images function below to guide your construction of the function:

In [39]:
import os

def save_images(imgs_details_df, imgs_dataset, target_folderpath):
    '''
    This function takes the following inputs and saves all images to the 'target_folder' in the local directory
    inputs:
    imgs_details_df: DataFrame containing details of images like filenames, finelabels (specific image labels) 
                     and row number details 
    imgs_dataset   : Original CIFAR-100 images dataset containing image data in the row major form that further 
                     needs to be transformed into 3 channeled image array
    target_folder  : Output folder where the transformed images are saved
    '''
    
    # Saving dataset 'row' numbers of corresponding images from 'imgs_details_df' dataframe
    row_number_imgs_data = imgs_details_df['row'] 
    
    # Corresponding image filenames in the dataset
    imgs_file_names = imgs_details_df['filenames']
    
    for row, img_name in zip(row_number_imgs_data, imgs_file_names):
        
        #Grab the image data in row-major form
        img = imgs_dataset[b'data'][row]

        # Consolidated stacking/reshaping from earlier (For all 3-R,G,B channels)
        target = np.dstack((
                        img[0:1024].reshape(32,32,1),
                        img[1024:2048].reshape(32,32,1),
                        img[2048:].reshape(32,32,1)
                         ))
    
        # Save the image to target folder (local directory)
        try:
            save_img_filepath = os.path.join(target_folderpath, img_name)
            plt.imsave(save_img_filepath, target)
            
            # For printing the saved image filepath in correct format
            raw_save_img_filepath = r"{}".format(save_img_filepath)
            nraw_save_img_filepath = os.path.normpath(raw_save_img_filepath)
            print(f"Saved: {img_name} to {nraw_save_img_filepath}")
            
        # Return any signal data you want for debugging
        except RuntimeError as e:
            return f"Error: {e} \nAn error encountered while saving {img_name} file to the target folder located at {target_folderpath}"

## TODO: save ALL images using the save_images function

# Save all transformed train dataset images in "./train"
save_images(imgs_details_df= df_train,
            imgs_dataset= dataset_train, 
            target_folderpath= "./train")

# Save all transformed test dataset images in "./test"
save_images(imgs_details_df= df_test,
            imgs_dataset= dataset_test, 
            target_folderpath= "./test")

Saved: bike_s_000682.png to train/bike_s_000682.png
Saved: bike_s_000127.png to train/bike_s_000127.png
Saved: cycle_s_002598.png to train/cycle_s_002598.png
Saved: minibike_s_000824.png to train/minibike_s_000824.png
Saved: motorcycle_s_001856.png to train/motorcycle_s_001856.png
Saved: bicycle_s_000537.png to train/bicycle_s_000537.png
Saved: moped_s_000169.png to train/moped_s_000169.png
Saved: motorcycle_s_001453.png to train/motorcycle_s_001453.png
Saved: safety_bike_s_000482.png to train/safety_bike_s_000482.png
Saved: dirt_bike_s_000124.png to train/dirt_bike_s_000124.png
Saved: minibike_s_001863.png to train/minibike_s_001863.png
Saved: minibike_s_001177.png to train/minibike_s_001177.png
Saved: motorcycle_s_001792.png to train/motorcycle_s_001792.png
Saved: minibike_s_001969.png to train/minibike_s_001969.png
Saved: velocipede_s_001585.png to train/velocipede_s_001585.png
Saved: moped_s_000035.png to train/moped_s_000035.png
Saved: bike_s_000256.png to train/bike_s_000256.png
Saved: minibike_s_000571.png to train/minibike_s_000571.png
Saved: motorcycle_s_001119.png to train/motorcycle_s_001119.png
Saved: moped_s_000009.png to train/moped_s_000009.png
Saved: ordinary_bicycle_s_000023.png to train/ordinary_bicycle_s_000023.png
Saved: motorcycle_s_001693.png to train/motorcycle_s_001693.png
Saved: safety_bicycle_s_001570.png to train/safety_bicycle_s_001570.png
Saved: safety_bike_s_000989.png to train/safety_bike_s_000989.png
Saved: bicycle_s_001673.png to train/bicycle_s_001673.png
Saved: ordinary_bicycle_s_000100.png to train/ordinary_bicycle_s_000100.png
Saved: bike_s_000544.png to train/bike_s_000544.png
Saved: motorcycle_s_001368.png to train/motorcycle_s_001368.png
Saved: bicycle_s_000392.png to train/bicycle_s_000392.png
Saved: motorbike_s_000221.png to train/motorbike_s_000221.png
Saved: safety_bike_s_000311.png to train/safety_bike_s_000311.png
Saved: cycle_s_001745.png to train/cycle_s_001745.png
Saved: motorcycle_s_002030.png to train/motorcycle_s_002030.png
Saved: motorcycle_s_000074.png to train/motorcycle_s_000074.png
Saved: cycle_s_002978.png to train/cycle_s_002978.png
Saved: bike_s_001679.png to train/bike_s_001679.png
Saved: bicycle_s_000536.png to train/bicycle_s_000536.png
Saved: bike_s_000926.png to train/bike_s_000926.png
Saved: motorcycle_s_000070.png to train/motorcycle_s_000070.png
Saved: safety_bicycle_s_000655.png to train/safety_bicycle_s_000655.png
Saved: safety_bicycle_s_000427.png to train/safety_bicycle_s_000427.png
Saved: minibike_s_000324.png to train/minibike_s_000324.png
Saved: minibike_s_001631.png to train/minibike_s_001631.png
Saved: motorcycle_s_000629.png to train/motorcycle_s_000629.png
Saved: motorcycle_s_000605.png to train/motorcycle_s_000605.png
Saved: motorcycle_s_001973.png to train/motorcycle_s_001973.png
Saved: bike_s_000555.png to train/bike_s_000555.png
Saved: bicycle_s_002762.png to train/bicycle_s_002762.png
Saved: bicycle_s_000778.png to train/bicycle_s_000778.png
Saved: bike_s_001613.png to train/bike_s_001613.png
Saved: motorcycle_s_001180.png to train/motorcycle_s_001180.png
Saved: minibike_s_001279.png to train/minibike_s_001279.png
Saved: minibike_s_000451.png to train/minibike_s_000451.png
Saved: motorbike_s_000346.png to train/motorbike_s_000346.png
Saved: minibike_s_000117.png to train/minibike_s_000117.png
Saved: bicycle_s_001448.png to train/bicycle_s_001448.png
Saved: motorcycle_s_000790.png to train/motorcycle_s_000790.png
Saved: bicycle_s_002373.png to train/bicycle_s_002373.png
Saved: safety_bicycle_s_001202.png to train/safety_bicycle_s_001202.png
Saved: motorcycle_s_000001.png to train/motorcycle_s_000001.png
Saved: bike_s_001882.png to train/bike_s_001882.png
Saved: bike_s_001113.png to train/bike_s_001113.png
Saved: minibike_s_000362.png to train/minibike_s_000362.png
Saved: cycle_s_000718.png to train/cycle_s_000718.png
Saved: velocipede_s_001338.png to train/velocipede_s_001338.png
Saved: minibike_s_001972.png to train/minibike_s_001972.png
Saved: ordinary_bicycle_s_000107.png to train/ordinary_bicycle_s_000107.png
Saved: motorcycle_s_000776.png to train/motorcycle_s_000776.png
Saved: safety_bike_s_000934.png to train/safety_bike_s_000934.png
Saved: bicycle_s_002218.png to train/bicycle_s_002218.png
Saved: safety_bicycle_s_001240.png to train/safety_bicycle_s_001240.png
Saved: ordinary_bicycle_s_000355.png to train/ordinary_bicycle_s_000355.png
Saved: motorcycle_s_000454.png to train/motorcycle_s_000454.png
Saved: motorcycle_s_000245.png to train/motorcycle_s_000245.png
Saved: minibike_s_000743.png to train/minibike_s_000743.png
Saved: safety_bike_s_000830.png to train/safety_bike_s_000830.png
Saved: minibike_s_001573.png to train/minibike_s_001573.png
Saved: motorbike_s_000141.png to train/motorbike_s_000141.png
Saved: minibike_s_000652.png to train/minibike_s_000652.png
Saved: motorbike_s_000058.png to train/motorbike_s_000058.png
Saved: bicycle_s_000279.png to train/bicycle_s_000279.png
Saved: safety_bike_s_001608.png to train/safety_bike_s_001608.png
Saved: bicycle_s_000522.png to train/bicycle_s_000522.png
Saved: bicycle_s_002569.png to train/bicycle_s_002569.png
Saved: bicycle_s_000829.png to train/bicycle_s_000829.png
Saved: velocipede_s_000586.png to train/velocipede_s_000586.png
Saved: dirt_bike_s_000017.png to train/dirt_bike_s_000017.png
Saved: cycle_s_001640.png to train/cycle_s_001640.png
Saved: bicycle_s_000282.png to train/bicycle_s_000282.png
Saved: motorcycle_s_000713.png to train/motorcycle_s_000713.png
Saved: bike_s_000035.png to train/bike_s_000035.png
Saved: bike_s_000299.png to train/bike_s_000299.png
Saved: minibike_s_002198.png to train/minibike_s_002198.png
Saved: safety_bicycle_s_001253.png to train/safety_bicycle_s_001253.png
Saved: minibike_s_000110.png to train/minibike_s_000110.png
Saved: motorcycle_s_001370.png to train/motorcycle_s_001370.png
Saved: motorcycle_s_001363.png to train/motorcycle_s_001363.png
Saved: ordinary_bicycle_s_000060.png to train/ordinary_bicycle_s_000060.png
Saved: minibike_s_001653.png to train/minibike_s_001653.png
Saved: bicycle_s_001338.png to train/bicycle_s_001338.png
Saved: cycle_s_002053.png to train/cycle_s_002053.png
Saved: velocipede_s_001056.png to train/velocipede_s_001056.png
Saved: minibike_s_000402.png to train/minibike_s_000402.png
Saved: motorbike_s_000124.png to train/motorbike_s_000124.png
Saved: cycle_s_002638.png to train/cycle_s_002638.png
Saved: velocipede_s_000949.png to train/velocipede_s_000949.png
Saved: safety_bike_s_000921.png to train/safety_bike_s_000921.png
Saved: ordinary_bicycle_s_000297.png to train/ordinary_bicycle_s_000297.png
Saved: motorcycle_s_001580.png to train/motorcycle_s_001580.png
Saved: velocipede_s_000139.png to train/velocipede_s_000139.png
Saved: bicycle_s_000561.png to train/bicycle_s_000561.png
Saved: ordinary_bicycle_s_000161.png to train/ordinary_bicycle_s_000161.png
Saved: minibike_s_000518.png to train/minibike_s_000518.png
Saved: motorcycle_s_000167.png to train/motorcycle_s_000167.png
Saved: cycle_s_000318.png to train/cycle_s_000318.png
Saved: velocipede_s_001958.png to train/velocipede_s_001958.png
Saved: ordinary_bicycle_s_000201.png to train/ordinary_bicycle_s_000201.png
Saved: bike_s_001462.png to train/bike_s_001462.png
Saved: bicycle_s_000437.png to train/bicycle_s_000437.png
Saved: velocipede_s_000825.png to train/velocipede_s_000825.png
Saved: safety_bike_s_000100.png to train/safety_bike_s_000100.png
Saved: minibike_s_000654.png to train/minibike_s_000654.png
Saved: bicycle_s_000021.png to train/bicycle_s_000021.png
Saved: moped_s_000030.png to train/moped_s_000030.png
Saved: velocipede_s_001142.png to train/velocipede_s_001142.png
Saved: motorbike_s_000117.png to train/motorbike_s_000117.png
Saved: ordinary_bicycle_s_000036.png to train/ordinary_bicycle_s_000036.png
Saved: motorcycle_s_000787.png to train/motorcycle_s_000787.png
Saved: bike_s_000516.png to train/bike_s_000516.png
Saved: motorcycle_s_000256.png to train/motorcycle_s_000256.png
Saved: bicycle_s_002299.png to train/bicycle_s_002299.png
Saved: velocipede_s_001883.png to train/velocipede_s_001883.png
Saved: motorcycle_s_002143.png to train/motorcycle_s_002143.png
Saved: cycle_s_000899.png to train/cycle_s_000899.png
Saved: bicycle_s_002436.png to train/bicycle_s_002436.png
Saved: safety_bicycle_s_000255.png to train/safety_bicycle_s_000255.png
Saved: minibike_s_000565.png to train/minibike_s_000565.png
Saved: bike_s_000034.png to train/bike_s_000034.png
Saved: motorcycle_s_001064.png to train/motorcycle_s_001064.png
Saved: minibike_s_001017.png to train/minibike_s_001017.png
Saved: motorbike_s_000172.png to train/motorbike_s_000172.png
Saved: motorcycle_s_000923.png to train/motorcycle_s_000923.png
Saved: cycle_s_000583.png to train/cycle_s_000583.png
Saved: motorcycle_s_000991.png to train/motorcycle_s_000991.png
Saved: motorbike_s_000541.png to train/motorbike_s_000541.png
Saved: motorcycle_s_001303.png to train/motorcycle_s_001303.png
Saved: safety_bike_s_000567.png to train/safety_bike_s_000567.png
Saved: minibike_s_000350.png to train/minibike_s_000350.png
Saved: motorcycle_s_001905.png to train/motorcycle_s_001905.png
Saved: bicycle_s_001247.png to train/bicycle_s_001247.png
Saved: bicycle_s_001693.png to train/bicycle_s_001693.png
Saved: bicycle_s_002258.png to train/bicycle_s_002258.png
Saved: safety_bike_s_001552.png to train/safety_bike_s_001552.png
Saved: cycle_s_002651.png to train/cycle_s_002651.png
Saved: bicycle_s_000759.png to train/bicycle_s_000759.png
Saved: motorcycle_s_001176.png to train/motorcycle_s_001176.png
Saved: ordinary_bicycle_s_000432.png to train/ordinary_bicycle_s_000432.png
Saved: minibike_s_000526.png to train/minibike_s_000526.png
Saved: safety_bicycle_s_001049.png to train/safety_bicycle_s_001049.png
Saved: minibike_s_001827.png to train/minibike_s_001827.png
Saved: bike_s_001072.png to train/bike_s_001072.png
Saved: bike_s_001876.png to train/bike_s_001876.png
Saved: minibike_s_001511.png to train/minibike_s_001511.png
Saved: bike_s_000237.png to train/bike_s_000237.png
Saved: motorbike_s_000254.png to train/motorbike_s_000254.png
Saved: bicycle_s_000180.png to train/bicycle_s_000180.png
Saved: safety_bicycle_s_000359.png to train/safety_bicycle_s_000359.png
Saved: motorcycle_s_000220.png to train/motorcycle_s_000220.png
Saved: motorcycle_s_002225.png to train/motorcycle_s_002225.png
Saved: minibike_s_001185.png to train/minibike_s_001185.png
Saved: motorcycle_s_000025.png to train/motorcycle_s_000025.png
Saved: safety_bike_s_000058.png to train/safety_bike_s_000058.png
Saved: dirt_bike_s_000030.png to train/dirt_bike_s_000030.png
Saved: minibike_s_002009.png to train/minibike_s_002009.png
Saved: bicycle_s_000753.png to train/bicycle_s_000753.png
Saved: safety_bicycle_s_001320.png to train/safety_bicycle_s_001320.png
Saved: motorcycle_s_001287.png to train/motorcycle_s_001287.png
Saved: ordinary_bicycle_s_000451.png to train/ordinary_bicycle_s_000451.png
Saved: minibike_s_000885.png to train/minibike_s_000885.png
Saved: motorcycle_s_000418.png to train/motorcycle_s_000418.png
Saved: velocipede_s_000282.png to train/velocipede_s_000282.png
Saved: cycle_s_001474.png to train/cycle_s_001474.png
Saved: bicycle_s_000667.png to train/bicycle_s_000667.png
Saved: minibike_s_001546.png to train/minibike_s_001546.png
Saved: bicycle_s_001452.png to train/bicycle_s_001452.png
Saved: motorcycle_s_000978.png to train/motorcycle_s_000978.png
Saved: motorcycle_s_000447.png to train/motorcycle_s_000447.png
Saved: minibike_s_000146.png to train/minibike_s_000146.png
Saved: motorcycle_s_000300.png to train/motorcycle_s_000300.png
Saved: safety_bicycle_s_001381.png to train/safety_bicycle_s_001381.png
Saved: bike_s_001519.png to train/bike_s_001519.png
Saved: minibike_s_001689.png to train/minibike_s_001689.png
Saved: bike_s_001320.png to train/bike_s_001320.png
Saved: bike_s_000722.png to train/bike_s_000722.png
Saved: motorcycle_s_000508.png to train/motorcycle_s_000508.png
Saved: motorcycle_s_002105.png to train/motorcycle_s_002105.png
Saved: minibike_s_000165.png to train/minibike_s_000165.png
Saved: cycle_s_002703.png to train/cycle_s_002703.png
Saved: ordinary_bicycle_s_000216.png to train/ordinary_bicycle_s_000216.png
Saved: motorcycle_s_001610.png to train/motorcycle_s_001610.png
Saved: cycle_s_002779.png to train/cycle_s_002779.png
Saved: bicycle_s_000668.png to train/bicycle_s_000668.png
Saved: minibike_s_002186.png to train/minibike_s_002186.png
Saved: bike_s_002118.png to train/bike_s_002118.png
Saved: safety_bike_s_001472.png to train/safety_bike_s_001472.png
Saved: ordinary_bicycle_s_000016.png to train/ordinary_bicycle_s_000016.png
Saved: motorcycle_s_001384.png to train/motorcycle_s_001384.png
Saved: motorcycle_s_000431.png to train/motorcycle_s_000431.png
Saved: bike_s_000302.png to train/bike_s_000302.png
Saved: velocipede_s_000659.png to train/velocipede_s_000659.png
Saved: motorcycle_s_001585.png to train/motorcycle_s_001585.png
Saved: bike_s_001027.png to train/bike_s_001027.png
Saved: bicycle_s_000396.png to train/bicycle_s_000396.png
Saved: minibike_s_000149.png to train/minibike_s_000149.png
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Saved: cycle_s_003122.png to train/cycle_s_003122.png
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Saved: safety_bicycle_s_001026.png to train/safety_bicycle_s_001026.png
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Saved: safety_bicycle_s_000533.png to train/safety_bicycle_s_000533.png
Saved: bike_s_000021.png to train/bike_s_000021.png
Saved: minibike_s_000968.png to train/minibike_s_000968.png
Saved: minibike_s_001873.png to train/minibike_s_001873.png
Saved: bicycle_s_000463.png to train/bicycle_s_000463.png
Saved: bike_s_001839.png to train/bike_s_001839.png
Saved: minibike_s_000340.png to train/minibike_s_000340.png
Saved: minibike_s_000831.png to train/minibike_s_000831.png
Saved: motorcycle_s_001273.png to train/motorcycle_s_001273.png
Saved: motorcycle_s_000807.png to train/motorcycle_s_000807.png
Saved: bicycle_s_000156.png to train/bicycle_s_000156.png
Saved: motorcycle_s_000732.png to train/motorcycle_s_000732.png
Saved: dirt_bike_s_000119.png to train/dirt_bike_s_000119.png
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Saved: ordinary_bicycle_s_000056.png to train/ordinary_bicycle_s_000056.png
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Saved: safety_bicycle_s_001243.png to train/safety_bicycle_s_001243.png
Saved: motorcycle_s_000894.png to train/motorcycle_s_000894.png
Saved: motorcycle_s_001397.png to train/motorcycle_s_001397.png
Saved: motorcycle_s_000542.png to train/motorcycle_s_000542.png
Saved: motorcycle_s_000834.png to train/motorcycle_s_000834.png
Saved: motorcycle_s_001238.png to train/motorcycle_s_001238.png
Saved: minibike_s_000567.png to train/minibike_s_000567.png
Saved: minibike_s_001512.png to train/minibike_s_001512.png
Saved: bike_s_001945.png to train/bike_s_001945.png
Saved: cycle_s_003148.png to train/cycle_s_003148.png
Saved: safety_bicycle_s_000019.png to train/safety_bicycle_s_000019.png
Saved: safety_bicycle_s_000167.png to train/safety_bicycle_s_000167.png
Saved: motorcycle_s_001407.png to train/motorcycle_s_001407.png
Saved: velocipede_s_001225.png to train/velocipede_s_001225.png
Saved: moped_s_000168.png to train/moped_s_000168.png
Saved: bicycle_s_000071.png to train/bicycle_s_000071.png
Saved: minibike_s_002030.png to train/minibike_s_002030.png
Saved: minibike_s_001294.png to train/minibike_s_001294.png
Saved: minibike_s_001767.png to train/minibike_s_001767.png
Saved: safety_bike_s_000984.png to train/safety_bike_s_000984.png
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Saved: ordinary_bicycle_s_000095.png to train/ordinary_bicycle_s_000095.png
Saved: cycle_s_001286.png to train/cycle_s_001286.png
Saved: bike_s_001116.png to train/bike_s_001116.png
Saved: motorcycle_s_002065.png to train/motorcycle_s_002065.png
Saved: ordinary_bicycle_s_000433.png to train/ordinary_bicycle_s_000433.png
Saved: motorcycle_s_002221.png to train/motorcycle_s_002221.png
Saved: safety_bike_s_000054.png to train/safety_bike_s_000054.png
Saved: bicycle_s_001174.png to train/bicycle_s_001174.png
Saved: bicycle_s_002222.png to train/bicycle_s_002222.png
Saved: velocipede_s_001361.png to train/velocipede_s_001361.png
Saved: safety_bike_s_001012.png to train/safety_bike_s_001012.png
Saved: minibike_s_000244.png to train/minibike_s_000244.png
Saved: bicycle_s_000861.png to train/bicycle_s_000861.png
Saved: minibike_s_000566.png to train/minibike_s_000566.png
Saved: motorcycle_s_001244.png to train/motorcycle_s_001244.png
Saved: bicycle_s_002715.png to train/bicycle_s_002715.png
Saved: safety_bike_s_000963.png to train/safety_bike_s_000963.png
Saved: motorcycle_s_001765.png to train/motorcycle_s_001765.png
Saved: velocipede_s_000021.png to train/velocipede_s_000021.png
Saved: motorcycle_s_000580.png to train/motorcycle_s_000580.png
Saved: bike_s_001226.png to train/bike_s_001226.png
Saved: motorcycle_s_000440.png to train/motorcycle_s_000440.png
Saved: motorcycle_s_000144.png to train/motorcycle_s_000144.png
Saved: cycle_s_002844.png to train/cycle_s_002844.png
Saved: minibike_s_000485.png to train/minibike_s_000485.png
Saved: ordinary_bicycle_s_000426.png to train/ordinary_bicycle_s_000426.png
Saved: minibike_s_002196.png to train/minibike_s_002196.png
Saved: bike_s_001897.png to train/bike_s_001897.png
Saved: cycle_s_001374.png to train/cycle_s_001374.png
Saved: motorcycle_s_000585.png to train/motorcycle_s_000585.png
Saved: bike_s_000026.png to train/bike_s_000026.png
Saved: bicycle_s_000235.png to train/bicycle_s_000235.png
Saved: minibike_s_000108.png to train/minibike_s_000108.png
Saved: motorcycle_s_000072.png to train/motorcycle_s_000072.png
Saved: motorcycle_s_002234.png to train/motorcycle_s_002234.png
Saved: bicycle_s_000785.png to train/bicycle_s_000785.png
Saved: motorcycle_s_000669.png to train/motorcycle_s_000669.png
Saved: bicycle_s_000051.png to train/bicycle_s_000051.png
Saved: bike_s_001093.png to train/bike_s_001093.png
Saved: velocipede_s_000049.png to train/velocipede_s_000049.png
Saved: bike_s_000023.png to train/bike_s_000023.png
Saved: motorcycle_s_001696.png to train/motorcycle_s_001696.png
Saved: safety_bicycle_s_001113.png to train/safety_bicycle_s_001113.png
Saved: motorcycle_s_000451.png to train/motorcycle_s_000451.png
Saved: velocipede_s_000485.png to train/velocipede_s_000485.png
Saved: motorcycle_s_001753.png to train/motorcycle_s_001753.png
Saved: cycle_s_001439.png to train/cycle_s_001439.png
Saved: minibike_s_002038.png to train/minibike_s_002038.png
Saved: bicycle_s_000147.png to train/bicycle_s_000147.png
Saved: cycle_s_002399.png to train/cycle_s_002399.png
Saved: motorcycle_s_001565.png to train/motorcycle_s_001565.png
Saved: bicycle_s_000159.png to train/bicycle_s_000159.png
Saved: minibike_s_000035.png to train/minibike_s_000035.png
Saved: safety_bicycle_s_000239.png to train/safety_bicycle_s_000239.png
Saved: safety_bike_s_001640.png to train/safety_bike_s_001640.png
Saved: minibike_s_000698.png to train/minibike_s_000698.png
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Saved: motorcycle_s_000216.png to train/motorcycle_s_000216.png
Saved: bike_s_000855.png to train/bike_s_000855.png
Saved: motorcycle_s_001715.png to train/motorcycle_s_001715.png
Saved: motorcycle_s_002275.png to train/motorcycle_s_002275.png
Saved: safety_bike_s_001338.png to train/safety_bike_s_001338.png
Saved: safety_bicycle_s_001705.png to train/safety_bicycle_s_001705.png
Saved: minibike_s_000855.png to train/minibike_s_000855.png
Saved: bicycle_s_002132.png to train/bicycle_s_002132.png
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Saved: motorcycle_s_001392.png to train/motorcycle_s_001392.png
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Saved: motorbike_s_000362.png to train/motorbike_s_000362.png
Saved: minibike_s_000519.png to train/minibike_s_000519.png
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Saved: cycle_s_002090.png to train/cycle_s_002090.png
Saved: bicycle_s_001168.png to train/bicycle_s_001168.png
Saved: minibike_s_000498.png to train/minibike_s_000498.png
Saved: motorcycle_s_001400.png to train/motorcycle_s_001400.png
Saved: motorcycle_s_000320.png to train/motorcycle_s_000320.png
Saved: minibike_s_001734.png to train/minibike_s_001734.png
Saved: cycle_s_002659.png to train/cycle_s_002659.png
Saved: bike_s_000990.png to train/bike_s_000990.png
Saved: velocipede_s_000358.png to train/velocipede_s_000358.png
Saved: safety_bicycle_s_000568.png to train/safety_bicycle_s_000568.png
Saved: motorcycle_s_000312.png to train/motorcycle_s_000312.png
Saved: motorcycle_s_001686.png to train/motorcycle_s_001686.png
Saved: cycle_s_001472.png to train/cycle_s_001472.png
Saved: minibike_s_000064.png to train/minibike_s_000064.png
Saved: safety_bike_s_001784.png to train/safety_bike_s_001784.png
Saved: motorcycle_s_002236.png to train/motorcycle_s_002236.png
Saved: minibike_s_000020.png to train/minibike_s_000020.png
Saved: motorcycle_s_001182.png to train/motorcycle_s_001182.png
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Saved: ordinary_bicycle_s_000167.png to train/ordinary_bicycle_s_000167.png
Saved: cycle_s_002666.png to train/cycle_s_002666.png
Saved: dirt_bike_s_000134.png to train/dirt_bike_s_000134.png
Saved: bike_s_000015.png to train/bike_s_000015.png
Saved: bike_s_001767.png to train/bike_s_001767.png
Saved: motorcycle_s_001790.png to train/motorcycle_s_001790.png
Saved: bike_s_001915.png to train/bike_s_001915.png
Saved: bike_s_001415.png to train/bike_s_001415.png
Saved: motorbike_s_000578.png to train/motorbike_s_000578.png
Saved: safety_bicycle_s_000532.png to train/safety_bicycle_s_000532.png
Saved: ordinary_bicycle_s_000049.png to train/ordinary_bicycle_s_000049.png
Saved: minibike_s_001639.png to train/minibike_s_001639.png
Saved: bike_s_000129.png to train/bike_s_000129.png
Saved: minibike_s_001565.png to train/minibike_s_001565.png
Saved: bike_s_002203.png to train/bike_s_002203.png
Saved: minibike_s_000401.png to train/minibike_s_000401.png
Saved: safety_bicycle_s_001608.png to train/safety_bicycle_s_001608.png
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Saved: safety_bicycle_s_000207.png to train/safety_bicycle_s_000207.png
Saved: motorcycle_s_001853.png to train/motorcycle_s_001853.png
Saved: bike_s_002222.png to train/bike_s_002222.png
Saved: motorcycle_s_000004.png to train/motorcycle_s_000004.png
Saved: motorcycle_s_000496.png to train/motorcycle_s_000496.png
Saved: motorcycle_s_001492.png to train/motorcycle_s_001492.png
Saved: bike_s_001761.png to train/bike_s_001761.png
Saved: velocipede_s_001299.png to train/velocipede_s_001299.png
Saved: bike_s_000040.png to train/bike_s_000040.png
Saved: cycle_s_003162.png to train/cycle_s_003162.png
Saved: velocipede_s_001880.png to train/velocipede_s_001880.png
Saved: bike_s_002139.png to train/bike_s_002139.png
Saved: bicycle_s_001583.png to train/bicycle_s_001583.png
Saved: safety_bicycle_s_001706.png to train/safety_bicycle_s_001706.png
Saved: bike_s_001200.png to train/bike_s_001200.png
Saved: motorcycle_s_000699.png to train/motorcycle_s_000699.png
Saved: cycle_s_000492.png to train/cycle_s_000492.png
Saved: cycle_s_000222.png to train/cycle_s_000222.png
Saved: motorcycle_s_002162.png to train/motorcycle_s_002162.png
Saved: minibike_s_000409.png to train/minibike_s_000409.png
Saved: cycle_s_001735.png to train/cycle_s_001735.png
Saved: bike_s_000657.png to train/bike_s_000657.png
Saved: safety_bike_s_000643.png to train/safety_bike_s_000643.png
Saved: bike_s_000506.png to train/bike_s_000506.png
Saved: minibike_s_001348.png to train/minibike_s_001348.png
Saved: moped_s_000065.png to train/moped_s_000065.png
Saved: bicycle_s_002729.png to train/bicycle_s_002729.png
Saved: bicycle_s_001687.png to train/bicycle_s_001687.png
Saved: bicycle_s_002338.png to train/bicycle_s_002338.png
Saved: motorcycle_s_000459.png to train/motorcycle_s_000459.png
Saved: bike_s_000593.png to train/bike_s_000593.png
Saved: velocipede_s_001422.png to train/velocipede_s_001422.png
Saved: ordinary_bicycle_s_000347.png to train/ordinary_bicycle_s_000347.png
Saved: minibike_s_001870.png to train/minibike_s_001870.png
Saved: bicycle_s_001757.png to train/bicycle_s_001757.png
Saved: motorcycle_s_001016.png to train/motorcycle_s_001016.png
Saved: motorbike_s_000041.png to train/motorbike_s_000041.png
Saved: bike_s_000522.png to train/bike_s_000522.png
Saved: ordinary_bicycle_s_000104.png to train/ordinary_bicycle_s_000104.png
Saved: bike_s_000397.png to train/bike_s_000397.png
Saved: minibike_s_000802.png to train/minibike_s_000802.png
Saved: trail_bike_s_000016.png to train/trail_bike_s_000016.png
Saved: motorcycle_s_000686.png to train/motorcycle_s_000686.png
Saved: velocipede_s_001935.png to train/velocipede_s_001935.png
Saved: safety_bike_s_000165.png to train/safety_bike_s_000165.png
Saved: motorbike_s_000308.png to train/motorbike_s_000308.png
Saved: bike_s_000018.png to train/bike_s_000018.png
Saved: cycle_s_001976.png to train/cycle_s_001976.png
Saved: minibike_s_000741.png to train/minibike_s_000741.png
Saved: motorcycle_s_001236.png to train/motorcycle_s_001236.png
Saved: moped_s_000071.png to train/moped_s_000071.png
Saved: bike_s_000164.png to train/bike_s_000164.png
Saved: bike_s_001739.png to train/bike_s_001739.png
Saved: bicycle_s_001388.png to train/bicycle_s_001388.png
Saved: bicycle_s_000149.png to train/bicycle_s_000149.png
Saved: motorcycle_s_002197.png to train/motorcycle_s_002197.png
Saved: motorcycle_s_000277.png to train/motorcycle_s_000277.png
Saved: motorcycle_s_001690.png to train/motorcycle_s_001690.png
Saved: minibike_s_002048.png to train/minibike_s_002048.png
Saved: motorcycle_s_000253.png to train/motorcycle_s_000253.png
Saved: safety_bike_s_001010.png to train/safety_bike_s_001010.png
Saved: motorcycle_s_000785.png to train/motorcycle_s_000785.png
Saved: ordinary_bicycle_s_000387.png to train/ordinary_bicycle_s_000387.png
Saved: cycle_s_001309.png to train/cycle_s_001309.png
Saved: motorcycle_s_002316.png to train/motorcycle_s_002316.png
Saved: motorcycle_s_000340.png to train/motorcycle_s_000340.png
Saved: moped_s_000124.png to train/moped_s_000124.png
Saved: minibike_s_001917.png to train/minibike_s_001917.png
Saved: bicycle_s_000951.png to train/bicycle_s_000951.png
Saved: motorcycle_s_000002.png to train/motorcycle_s_000002.png
Saved: motorcycle_s_001063.png to train/motorcycle_s_001063.png
Saved: motorcycle_s_001641.png to train/motorcycle_s_001641.png
Saved: bicycle_s_002129.png to train/bicycle_s_002129.png
Saved: minibike_s_002182.png to train/minibike_s_002182.png
Saved: minibike_s_000427.png to train/minibike_s_000427.png
Saved: motorcycle_s_001438.png to train/motorcycle_s_001438.png
Saved: motorcycle_s_000126.png to train/motorcycle_s_000126.png
Saved: minibike_s_001865.png to train/minibike_s_001865.png
Saved: motorcycle_s_000456.png to train/motorcycle_s_000456.png
Saved: motorcycle_s_000252.png to train/motorcycle_s_000252.png
Saved: bicycle_s_000043.png to train/bicycle_s_000043.png
Saved: bicycle_s_001928.png to train/bicycle_s_001928.png
Saved: safety_bike_s_000173.png to train/safety_bike_s_000173.png
Saved: motorcycle_s_000696.png to train/motorcycle_s_000696.png
Saved: motorcycle_s_001413.png to train/motorcycle_s_001413.png
Saved: safety_bicycle_s_001303.png to train/safety_bicycle_s_001303.png
Saved: motorcycle_s_001220.png to train/motorcycle_s_001220.png
Saved: safety_bicycle_s_000079.png to train/safety_bicycle_s_000079.png
Saved: bicycle_s_001681.png to train/bicycle_s_001681.png
Saved: cycle_s_002015.png to train/cycle_s_002015.png
Saved: cycle_s_002746.png to train/cycle_s_002746.png
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Saved: safety_bicycle_s_000303.png to train/safety_bicycle_s_000303.png
Saved: bike_s_000392.png to train/bike_s_000392.png
Saved: minibike_s_000408.png to train/minibike_s_000408.png
Saved: safety_bike_s_001355.png to train/safety_bike_s_001355.png
Saved: motorcycle_s_000076.png to train/motorcycle_s_000076.png
Saved: safety_bicycle_s_000322.png to train/safety_bicycle_s_000322.png
Saved: bike_s_002090.png to train/bike_s_002090.png
Saved: motorcycle_s_000796.png to train/motorcycle_s_000796.png
Saved: motorbike_s_000225.png to train/motorbike_s_000225.png
Saved: motorcycle_s_000050.png to train/motorcycle_s_000050.png
Saved: bike_s_000474.png to train/bike_s_000474.png
Saved: motorcycle_s_000127.png to train/motorcycle_s_000127.png
Saved: motorcycle_s_002193.png to train/motorcycle_s_002193.png
Saved: bicycle_s_002374.png to train/bicycle_s_002374.png
Saved: minibike_s_001558.png to train/minibike_s_001558.png
Saved: velocipede_s_001279.png to train/velocipede_s_001279.png
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Saved: bicycle_s_001411.png to train/bicycle_s_001411.png
Saved: minibike_s_001072.png to train/minibike_s_001072.png
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Saved: safety_bike_s_000160.png to train/safety_bike_s_000160.png
Saved: minibike_s_000820.png to train/minibike_s_000820.png
Saved: ordinary_bicycle_s_000298.png to train/ordinary_bicycle_s_000298.png
Saved: bicycle_s_000314.png to train/bicycle_s_000314.png
Saved: motorcycle_s_001862.png to train/motorcycle_s_001862.png
Saved: minibike_s_001575.png to train/minibike_s_001575.png
Saved: minibike_s_000709.png to train/minibike_s_000709.png
Saved: velocipede_s_000931.png to train/velocipede_s_000931.png
Saved: safety_bike_s_000104.png to train/safety_bike_s_000104.png
Saved: motorcycle_s_001197.png to train/motorcycle_s_001197.png
Saved: motorbike_s_000115.png to train/motorbike_s_000115.png
Saved: bicycle_s_002728.png to train/bicycle_s_002728.png
Saved: safety_bike_s_001474.png to train/safety_bike_s_001474.png
Saved: bike_s_002024.png to train/bike_s_002024.png
Saved: cycle_s_000666.png to train/cycle_s_000666.png
Saved: motorcycle_s_000617.png to train/motorcycle_s_000617.png
Saved: safety_bike_s_000461.png to train/safety_bike_s_000461.png
Saved: bike_s_001572.png to train/bike_s_001572.png
Saved: motorcycle_s_000003.png to train/motorcycle_s_000003.png
Saved: bicycle_s_002029.png to train/bicycle_s_002029.png
Saved: minibike_s_000701.png to train/minibike_s_000701.png
Saved: motorbike_s_000009.png to train/motorbike_s_000009.png
Saved: minibike_s_001789.png to train/minibike_s_001789.png
Saved: motorcycle_s_000417.png to train/motorcycle_s_000417.png
Saved: motorcycle_s_000246.png to train/motorcycle_s_000246.png
Saved: minibike_s_000886.png to train/minibike_s_000886.png
Saved: motorcycle_s_000154.png to train/motorcycle_s_000154.png
Saved: minibike_s_001169.png to train/minibike_s_001169.png
Saved: cycle_s_002904.png to train/cycle_s_002904.png
Saved: motorcycle_s_000772.png to train/motorcycle_s_000772.png
Saved: velocipede_s_001158.png to train/velocipede_s_001158.png
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Saved: minibike_s_001179.png to train/minibike_s_001179.png
Saved: bicycle_s_001111.png to train/bicycle_s_001111.png
Saved: bicycle_s_001205.png to train/bicycle_s_001205.png
Saved: bicycle_s_000435.png to train/bicycle_s_000435.png
Saved: minibike_s_001771.png to train/minibike_s_001771.png
Saved: motorcycle_s_000545.png to train/motorcycle_s_000545.png
Saved: moped_s_000004.png to train/moped_s_000004.png
Saved: velocipede_s_001358.png to train/velocipede_s_001358.png
Saved: motorcycle_s_001402.png to train/motorcycle_s_001402.png
Saved: minibike_s_001836.png to train/minibike_s_001836.png
Saved: motorcycle_s_002067.png to train/motorcycle_s_002067.png
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Saved: motorcycle_s_000517.png to train/motorcycle_s_000517.png
Saved: minibike_s_001345.png to train/minibike_s_001345.png
Saved: minibike_s_000522.png to train/minibike_s_000522.png
Saved: motorcycle_s_000700.png to train/motorcycle_s_000700.png
Saved: motorcycle_s_001183.png to train/motorcycle_s_001183.png
Saved: velocipede_s_001874.png to train/velocipede_s_001874.png
Saved: cycle_s_003006.png to train/cycle_s_003006.png
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Saved: ordinary_bicycle_s_000419.png to train/ordinary_bicycle_s_000419.png
Saved: motorcycle_s_000284.png to train/motorcycle_s_000284.png
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Saved: motorcycle_s_001784.png to train/motorcycle_s_001784.png
Saved: motorcycle_s_001848.png to train/motorcycle_s_001848.png
Saved: minibike_s_002046.png to train/minibike_s_002046.png
Saved: motorcycle_s_002317.png to train/motorcycle_s_002317.png
Saved: bike_s_001980.png to train/bike_s_001980.png
Saved: safety_bike_s_000950.png to train/safety_bike_s_000950.png
Saved: bicycle_s_002237.png to train/bicycle_s_002237.png
Saved: cycle_s_000042.png to train/cycle_s_000042.png
Saved: bike_s_000628.png to train/bike_s_000628.png
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Saved: minibike_s_001157.png to train/minibike_s_001157.png
Saved: bike_s_000051.png to train/bike_s_000051.png
Saved: motorcycle_s_000919.png to train/motorcycle_s_000919.png
Saved: velocipede_s_001514.png to train/velocipede_s_001514.png
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Saved: bicycle_s_001470.png to train/bicycle_s_001470.png
Saved: motorcycle_s_001695.png to train/motorcycle_s_001695.png
Saved: motorcycle_s_001215.png to train/motorcycle_s_001215.png
Saved: bike_s_002098.png to train/bike_s_002098.png
Saved: bike_s_001928.png to train/bike_s_001928.png
Saved: bicycle_s_001748.png to train/bicycle_s_001748.png
Saved: motorcycle_s_001520.png to train/motorcycle_s_001520.png
Saved: safety_bicycle_s_000728.png to train/safety_bicycle_s_000728.png
Saved: motorcycle_s_000925.png to train/motorcycle_s_000925.png
Saved: safety_bike_s_000914.png to train/safety_bike_s_000914.png
Saved: safety_bike_s_001659.png to train/safety_bike_s_001659.png
Saved: safety_bike_s_000155.png to train/safety_bike_s_000155.png
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Saved: safety_bike_s_001715.png to train/safety_bike_s_001715.png
Saved: bicycle_s_000035.png to train/bicycle_s_000035.png
Saved: bicycle_s_002669.png to train/bicycle_s_002669.png
Saved: bike_s_002277.png to train/bike_s_002277.png
Saved: ordinary_bicycle_s_000265.png to train/ordinary_bicycle_s_000265.png
Saved: bicycle_s_000369.png to train/bicycle_s_000369.png
Saved: velocipede_s_001298.png to train/velocipede_s_001298.png
Saved: minibike_s_001944.png to train/minibike_s_001944.png
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Saved: ordinary_bicycle_s_000008.png to train/ordinary_bicycle_s_000008.png
Saved: bicycle_s_000491.png to train/bicycle_s_000491.png
Saved: motorcycle_s_000146.png to train/motorcycle_s_000146.png
Saved: minibike_s_001261.png to train/minibike_s_001261.png
Saved: safety_bicycle_s_000140.png to train/safety_bicycle_s_000140.png
Saved: motorcycle_s_000407.png to train/motorcycle_s_000407.png
Saved: minibike_s_000099.png to train/minibike_s_000099.png
Saved: motorcycle_s_001767.png to train/motorcycle_s_001767.png
Saved: safety_bike_s_000263.png to train/safety_bike_s_000263.png
Saved: motorcycle_s_001205.png to train/motorcycle_s_001205.png
Saved: motorbike_s_000134.png to train/motorbike_s_000134.png
Saved: safety_bike_s_000245.png to train/safety_bike_s_000245.png
Saved: motorcycle_s_000422.png to train/motorcycle_s_000422.png
Saved: minibike_s_000800.png to train/minibike_s_000800.png
Saved: minibike_s_001829.png to train/minibike_s_001829.png
Saved: minibike_s_002124.png to train/minibike_s_002124.png
Saved: bike_s_001414.png to train/bike_s_001414.png
Saved: motorcycle_s_000423.png to train/motorcycle_s_000423.png
Saved: motorcycle_s_001435.png to train/motorcycle_s_001435.png
Saved: motorcycle_s_001237.png to train/motorcycle_s_001237.png
Saved: motorcycle_s_001878.png to train/motorcycle_s_001878.png
Saved: cycle_s_002093.png to train/cycle_s_002093.png
Saved: velocipede_s_000265.png to train/velocipede_s_000265.png
Saved: bike_s_001847.png to train/bike_s_001847.png
Saved: cycle_s_000463.png to train/cycle_s_000463.png
Saved: motorcycle_s_002140.png to train/motorcycle_s_002140.png
Saved: motorcycle_s_001246.png to train/motorcycle_s_001246.png
Saved: bike_s_001517.png to train/bike_s_001517.png
Saved: motorcycle_s_000219.png to train/motorcycle_s_000219.png
Saved: motorbike_s_000501.png to train/motorbike_s_000501.png
Saved: velocipede_s_001872.png to train/velocipede_s_001872.png
Saved: motorcycle_s_001337.png to train/motorcycle_s_001337.png
Saved: motorcycle_s_000169.png to train/motorcycle_s_000169.png
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Saved: ordinary_bicycle_s_000274.png to train/ordinary_bicycle_s_000274.png
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Saved: minibike_s_000010.png to train/minibike_s_000010.png
Saved: motorcycle_s_002121.png to train/motorcycle_s_002121.png
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Saved: bike_s_000001.png to train/bike_s_000001.png
Saved: minibike_s_001491.png to train/minibike_s_001491.png
Saved: motorcycle_s_000430.png to train/motorcycle_s_000430.png
Saved: motorcycle_s_000026.png to train/motorcycle_s_000026.png
Saved: bicycle_s_002448.png to train/bicycle_s_002448.png
Saved: cycle_s_001412.png to train/cycle_s_001412.png
Saved: motorcycle_s_000714.png to train/motorcycle_s_000714.png
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Saved: motorcycle_s_001126.png to train/motorcycle_s_001126.png
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Saved: motorcycle_s_002214.png to train/motorcycle_s_002214.png
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Saved: minibike_s_000568.png to train/minibike_s_000568.png
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Saved: minibike_s_001731.png to train/minibike_s_001731.png
Saved: bicycle_s_000243.png to train/bicycle_s_000243.png
Saved: motorcycle_s_000029.png to train/motorcycle_s_000029.png
Saved: velocipede_s_000023.png to train/velocipede_s_000023.png
Saved: minibike_s_001701.png to train/minibike_s_001701.png
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Saved: motorbike_s_000331.png to train/motorbike_s_000331.png
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Saved: velocipede_s_000989.png to train/velocipede_s_000989.png
Saved: moped_s_000135.png to train/moped_s_000135.png
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Saved: bike_s_002283.png to train/bike_s_002283.png
Saved: cycle_s_001413.png to train/cycle_s_001413.png
Saved: dirt_bike_s_000059.png to train/dirt_bike_s_000059.png
Saved: motorcycle_s_001699.png to train/motorcycle_s_001699.png
Saved: motorcycle_s_000687.png to train/motorcycle_s_000687.png
Saved: bike_s_000304.png to train/bike_s_000304.png
Saved: motorcycle_s_002192.png to train/motorcycle_s_002192.png
Saved: motorcycle_s_000887.png to train/motorcycle_s_000887.png
Saved: bike_s_002042.png to train/bike_s_002042.png
Saved: bicycle_s_000467.png to train/bicycle_s_000467.png
Saved: motorcycle_s_001706.png to train/motorcycle_s_001706.png
Saved: minibike_s_002176.png to train/minibike_s_002176.png
Saved: safety_bicycle_s_000760.png to train/safety_bicycle_s_000760.png
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Saved: safety_bike_s_000158.png to train/safety_bike_s_000158.png
Saved: bicycle_s_001679.png to train/bicycle_s_001679.png
Saved: motorcycle_s_000924.png to train/motorcycle_s_000924.png
Saved: bicycle_s_000996.png to train/bicycle_s_000996.png
Saved: motorcycle_s_000432.png to train/motorcycle_s_000432.png
Saved: ordinary_bicycle_s_000277.png to train/ordinary_bicycle_s_000277.png
Saved: bike_s_002164.png to train/bike_s_002164.png
Saved: minibike_s_000036.png to train/minibike_s_000036.png
Saved: motorbike_s_000062.png to train/motorbike_s_000062.png
Saved: motorcycle_s_000133.png to train/motorcycle_s_000133.png
Saved: safety_bike_s_000328.png to train/safety_bike_s_000328.png
Saved: safety_bicycle_s_001028.png to train/safety_bicycle_s_001028.png
Saved: minibike_s_001506.png to train/minibike_s_001506.png
Saved: motorcycle_s_001549.png to train/motorcycle_s_001549.png
Saved: bike_s_002292.png to train/bike_s_002292.png
Saved: bike_s_000121.png to train/bike_s_000121.png
Saved: motorcycle_s_001167.png to train/motorcycle_s_001167.png
Saved: bicycle_s_000124.png to train/bicycle_s_000124.png
Saved: motorcycle_s_002237.png to train/motorcycle_s_002237.png
Saved: velocipede_s_001870.png to train/velocipede_s_001870.png
Saved: moped_s_000119.png to train/moped_s_000119.png
Saved: minibike_s_000121.png to train/minibike_s_000121.png
Saved: motorcycle_s_000928.png to train/motorcycle_s_000928.png
Saved: minibike_s_001881.png to train/minibike_s_001881.png
Saved: bike_s_000679.png to train/bike_s_000679.png
Saved: motorcycle_s_002183.png to train/motorcycle_s_002183.png
Saved: bike_s_000111.png to train/bike_s_000111.png
Saved: bike_s_001260.png to train/bike_s_001260.png
Saved: safety_bicycle_s_000660.png to train/safety_bicycle_s_000660.png
Saved: motorcycle_s_000695.png to train/motorcycle_s_000695.png
Saved: safety_bicycle_s_000233.png to train/safety_bicycle_s_000233.png
Saved: motorcycle_s_000792.png to train/motorcycle_s_000792.png
Saved: motorcycle_s_000922.png to train/motorcycle_s_000922.png
Saved: bicycle_s_002711.png to train/bicycle_s_002711.png
Saved: bike_s_000390.png to train/bike_s_000390.png
Saved: minibike_s_001094.png to train/minibike_s_001094.png
Saved: minibike_s_000507.png to train/minibike_s_000507.png
Saved: motorcycle_s_001213.png to train/motorcycle_s_001213.png
Saved: bike_s_000154.png to train/bike_s_000154.png
Saved: motorcycle_s_000223.png to train/motorcycle_s_000223.png
Saved: bicycle_s_002661.png to train/bicycle_s_002661.png
Saved: safety_bicycle_s_000232.png to train/safety_bicycle_s_000232.png
Saved: bicycle_s_001663.png to train/bicycle_s_001663.png
Saved: motorbike_s_000463.png to train/motorbike_s_000463.png
Saved: minibike_s_002137.png to train/minibike_s_002137.png
Saved: safety_bicycle_s_001651.png to train/safety_bicycle_s_001651.png
Saved: motorcycle_s_000151.png to train/motorcycle_s_000151.png
Saved: motorcycle_s_000217.png to train/motorcycle_s_000217.png
Saved: bike_s_001877.png to train/bike_s_001877.png
Saved: velocipede_s_001278.png to train/velocipede_s_001278.png
Saved: minibike_s_001540.png to train/minibike_s_001540.png
Saved: minibike_s_001885.png to train/minibike_s_001885.png
Saved: velocipede_s_000012.png to train/velocipede_s_000012.png
Saved: bicycle_s_001956.png to train/bicycle_s_001956.png
Saved: ordinary_bicycle_s_000098.png to train/ordinary_bicycle_s_000098.png
Saved: bicycle_s_002725.png to train/bicycle_s_002725.png
Saved: velocipede_s_001210.png to train/velocipede_s_001210.png
Saved: bicycle_s_000039.png to train/bicycle_s_000039.png
Saved: motorcycle_s_000654.png to train/motorcycle_s_000654.png
Saved: safety_bike_s_000079.png to train/safety_bike_s_000079.png
Saved: motorcycle_s_002291.png to train/motorcycle_s_002291.png
Saved: cycle_s_002168.png to train/cycle_s_002168.png
Saved: bike_s_001078.png to train/bike_s_001078.png
Saved: motorbike_s_000427.png to train/motorbike_s_000427.png
Saved: minibike_s_001921.png to train/minibike_s_001921.png
Saved: motorcycle_s_000917.png to train/motorcycle_s_000917.png
Saved: velocipede_s_001920.png to train/velocipede_s_001920.png
Saved: cycle_s_003026.png to train/cycle_s_003026.png
Saved: motorcycle_s_000543.png to train/motorcycle_s_000543.png
Saved: minibike_s_000491.png to train/minibike_s_000491.png
Saved: motorcycle_s_001439.png to train/motorcycle_s_001439.png
Saved: minibike_s_002084.png to train/minibike_s_002084.png
Saved: bicycle_s_000775.png to train/bicycle_s_000775.png
Saved: motorcycle_s_000262.png to train/motorcycle_s_000262.png
Saved: moped_s_000237.png to train/moped_s_000237.png
Saved: bicycle_s_000978.png to train/bicycle_s_000978.png
Saved: bike_s_000162.png to train/bike_s_000162.png
Saved: motorbike_s_000106.png to train/motorbike_s_000106.png
Saved: motorcycle_s_000136.png to train/motorcycle_s_000136.png
Saved: motorcycle_s_001319.png to train/motorcycle_s_001319.png
Saved: safety_bicycle_s_000125.png to train/safety_bicycle_s_000125.png
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Saved: minibike_s_000511.png to train/minibike_s_000511.png
Saved: motorcycle_s_000332.png to train/motorcycle_s_000332.png
Saved: motorcycle_s_001315.png to train/motorcycle_s_001315.png
Saved: ordinary_bicycle_s_000031.png to train/ordinary_bicycle_s_000031.png
Saved: motorcycle_s_000353.png to train/motorcycle_s_000353.png
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Saved: safety_bicycle_s_000860.png to train/safety_bicycle_s_000860.png
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Saved: safety_bicycle_s_000324.png to train/safety_bicycle_s_000324.png
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Saved: motorbike_s_000068.png to train/motorbike_s_000068.png
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Saved: motorcycle_s_000989.png to train/motorcycle_s_000989.png
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Saved: ordinary_bicycle_s_000099.png to train/ordinary_bicycle_s_000099.png
Saved: safety_bike_s_001257.png to train/safety_bike_s_001257.png
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Saved: safety_bicycle_s_001085.png to train/safety_bicycle_s_001085.png
Saved: motorcycle_s_002222.png to train/motorcycle_s_002222.png
Saved: safety_bicycle_s_001639.png to train/safety_bicycle_s_001639.png
Saved: velocipede_s_001222.png to train/velocipede_s_001222.png
Saved: minibike_s_000392.png to train/minibike_s_000392.png
Saved: ordinary_bicycle_s_000308.png to train/ordinary_bicycle_s_000308.png
Saved: motorcycle_s_001896.png to train/motorcycle_s_001896.png
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Saved: safety_bicycle_s_000373.png to train/safety_bicycle_s_000373.png
Saved: safety_bicycle_s_000348.png to train/safety_bicycle_s_000348.png
Saved: bicycle_s_002005.png to train/bicycle_s_002005.png
Saved: minibike_s_002130.png to train/minibike_s_002130.png
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Saved: minibike_s_001193.png to train/minibike_s_001193.png
Saved: minibike_s_001366.png to train/minibike_s_001366.png
Saved: motorcycle_s_001711.png to train/motorcycle_s_001711.png
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Saved: velocipede_s_001907.png to train/velocipede_s_001907.png
Saved: motorcycle_s_000207.png to train/motorcycle_s_000207.png
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Saved: safety_bicycle_s_000261.png to train/safety_bicycle_s_000261.png
Saved: minibike_s_000738.png to train/minibike_s_000738.png
Saved: motorcycle_s_001106.png to train/motorcycle_s_001106.png
Saved: safety_bike_s_000198.png to train/safety_bike_s_000198.png
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Saved: velocipede_s_000430.png to train/velocipede_s_000430.png
Saved: bicycle_s_001409.png to train/bicycle_s_001409.png
Saved: motorcycle_s_000938.png to train/motorcycle_s_000938.png
Saved: motorcycle_s_000941.png to train/motorcycle_s_000941.png
Saved: bicycle_s_000038.png to train/bicycle_s_000038.png
Saved: minibike_s_001016.png to train/minibike_s_001016.png
Saved: velocipede_s_001200.png to train/velocipede_s_001200.png
Saved: bike_s_002109.png to train/bike_s_002109.png
Saved: cycle_s_002092.png to train/cycle_s_002092.png
Saved: motorcycle_s_002066.png to train/motorcycle_s_002066.png
Saved: motorcycle_s_001920.png to train/motorcycle_s_001920.png
Saved: minibike_s_000459.png to train/minibike_s_000459.png
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Saved: motorcycle_s_001611.png to train/motorcycle_s_001611.png
Saved: velocipede_s_001141.png to train/velocipede_s_001141.png
Saved: minibike_s_001747.png to train/minibike_s_001747.png
Saved: safety_bicycle_s_001029.png to train/safety_bicycle_s_001029.png
Saved: ordinary_bicycle_s_000029.png to train/ordinary_bicycle_s_000029.png
Saved: minibike_s_002163.png to train/minibike_s_002163.png
Saved: safety_bike_s_001148.png to train/safety_bike_s_001148.png
Saved: cycle_s_003008.png to train/cycle_s_003008.png
Saved: bicycle_s_000099.png to train/bicycle_s_000099.png
Saved: minibike_s_000429.png to train/minibike_s_000429.png
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Saved: cycle_s_002178.png to train/cycle_s_002178.png
Saved: cycle_s_000639.png to train/cycle_s_000639.png
Saved: motorcycle_s_000734.png to train/motorcycle_s_000734.png
Saved: motorcycle_s_000309.png to train/motorcycle_s_000309.png
Saved: bicycle_s_000371.png to train/bicycle_s_000371.png
Saved: minibike_s_001270.png to train/minibike_s_001270.png
Saved: minibike_s_000914.png to train/minibike_s_000914.png
Saved: moped_s_000021.png to train/moped_s_000021.png
Saved: motorcycle_s_000242.png to train/motorcycle_s_000242.png
Saved: minibike_s_000314.png to train/minibike_s_000314.png
Saved: motorcycle_s_000698.png to train/motorcycle_s_000698.png
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Saved: ordinary_bicycle_s_000157.png to train/ordinary_bicycle_s_000157.png
Saved: minibike_s_001838.png to train/minibike_s_001838.png
Saved: bicycle_s_002247.png to train/bicycle_s_002247.png
Saved: motorcycle_s_000027.png to train/motorcycle_s_000027.png
Saved: minibike_s_000906.png to train/minibike_s_000906.png
Saved: safety_bicycle_s_000500.png to train/safety_bicycle_s_000500.png
Saved: minibike_s_002146.png to train/minibike_s_002146.png
Saved: motorcycle_s_001242.png to train/motorcycle_s_001242.png
Saved: motorcycle_s_001707.png to train/motorcycle_s_001707.png
Saved: cycle_s_003043.png to train/cycle_s_003043.png
Saved: safety_bicycle_s_001109.png to train/safety_bicycle_s_001109.png
Saved: bike_s_001131.png to train/bike_s_001131.png
Saved: motorcycle_s_001118.png to train/motorcycle_s_001118.png
Saved: bike_s_001418.png to train/bike_s_001418.png
Saved: bicycle_s_002550.png to train/bicycle_s_002550.png
Saved: bicycle_s_002100.png to train/bicycle_s_002100.png
Saved: motorcycle_s_000933.png to train/motorcycle_s_000933.png
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Saved: safety_bicycle_s_001063.png to train/safety_bicycle_s_001063.png
Saved: motorbike_s_000361.png to train/motorbike_s_000361.png
Saved: safety_bicycle_s_000193.png to train/safety_bicycle_s_000193.png
Saved: safety_bicycle_s_000092.png to train/safety_bicycle_s_000092.png
Saved: motorcycle_s_000009.png to train/motorcycle_s_000009.png
Saved: bicycle_s_001554.png to train/bicycle_s_001554.png
Saved: motorcycle_s_001297.png to train/motorcycle_s_001297.png
Saved: safety_bicycle_s_000296.png to train/safety_bicycle_s_000296.png
Saved: safety_bike_s_001371.png to train/safety_bike_s_001371.png
Saved: bicycle_s_001569.png to train/bicycle_s_001569.png
Saved: motorcycle_s_001536.png to train/motorcycle_s_001536.png
Saved: safety_bicycle_s_001659.png to train/safety_bicycle_s_001659.png
Saved: ordinary_bicycle_s_000102.png to train/ordinary_bicycle_s_000102.png
Saved: bike_s_002215.png to train/bike_s_002215.png
Saved: bicycle_s_002410.png to train/bicycle_s_002410.png
Saved: cycle_s_003147.png to train/cycle_s_003147.png
Saved: bicycle_s_002759.png to train/bicycle_s_002759.png
Saved: minibike_s_000335.png to train/minibike_s_000335.png
Saved: safety_bike_s_000848.png to train/safety_bike_s_000848.png
Saved: velocipede_s_000807.png to train/velocipede_s_000807.png
Saved: bike_s_000024.png to train/bike_s_000024.png
Saved: minibike_s_000435.png to train/minibike_s_000435.png
Saved: minibike_s_001498.png to train/minibike_s_001498.png
Saved: velocipede_s_001166.png to train/velocipede_s_001166.png
Saved: motorcycle_s_001012.png to train/motorcycle_s_001012.png
Saved: velocipede_s_000330.png to train/velocipede_s_000330.png
Saved: motorcycle_s_000032.png to train/motorcycle_s_000032.png
Saved: minibike_s_001597.png to train/minibike_s_001597.png
Saved: motorcycle_s_002298.png to train/motorcycle_s_002298.png
Saved: minibike_s_000102.png to train/minibike_s_000102.png
Saved: motorcycle_s_001507.png to train/motorcycle_s_001507.png
Saved: motorbike_s_000022.png to train/motorbike_s_000022.png
Saved: motorcycle_s_000594.png to train/motorcycle_s_000594.png
Saved: minibike_s_001539.png to train/minibike_s_001539.png
Saved: bicycle_s_001228.png to train/bicycle_s_001228.png
Saved: minibike_s_001344.png to train/minibike_s_001344.png
Saved: motorcycle_s_002271.png to train/motorcycle_s_002271.png
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Saved: ordinary_bicycle_s_000218.png to train/ordinary_bicycle_s_000218.png
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Saved: minibike_s_001691.png to train/minibike_s_001691.png
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Saved: ordinary_bicycle_s_000101.png to train/ordinary_bicycle_s_000101.png
Saved: motorcycle_s_001326.png to train/motorcycle_s_001326.png
Saved: minibike_s_001249.png to train/minibike_s_001249.png
Saved: bicycle_s_000408.png to train/bicycle_s_000408.png
Saved: minibike_s_000520.png to train/minibike_s_000520.png
Saved: minibike_s_002218.png to train/minibike_s_002218.png
Saved: minibike_s_001458.png to train/minibike_s_001458.png
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Saved: motorcycle_s_001027.png to train/motorcycle_s_001027.png
Saved: cycle_s_001477.png to train/cycle_s_001477.png
Saved: motorcycle_s_001710.png to train/motorcycle_s_001710.png
Saved: minibike_s_000434.png to train/minibike_s_000434.png
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Saved: minibike_s_000527.png to train/minibike_s_000527.png
Saved: bicycle_s_001245.png to train/bicycle_s_001245.png
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Saved: bicycle_s_001002.png to train/bicycle_s_001002.png
Saved: moped_s_000044.png to train/moped_s_000044.png
Saved: bike_s_000025.png to train/bike_s_000025.png
Saved: minibike_s_000690.png to train/minibike_s_000690.png
Saved: minibike_s_000022.png to train/minibike_s_000022.png
Saved: motorcycle_s_001196.png to train/motorcycle_s_001196.png
Saved: ordinary_bicycle_s_000269.png to train/ordinary_bicycle_s_000269.png
Saved: cycle_s_002503.png to train/cycle_s_002503.png
Saved: ordinary_bicycle_s_000097.png to train/ordinary_bicycle_s_000097.png
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Saved: motorcycle_s_001017.png to train/motorcycle_s_001017.png
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Saved: velocipede_s_001489.png to train/velocipede_s_001489.png
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Saved: safety_bicycle_s_000425.png to train/safety_bicycle_s_000425.png
Saved: motorcycle_s_001622.png to train/motorcycle_s_001622.png
Saved: motorcycle_s_000593.png to train/motorcycle_s_000593.png
Saved: minibike_s_000116.png to train/minibike_s_000116.png
Saved: motorcycle_s_001563.png to train/motorcycle_s_001563.png
Saved: cycle_s_001875.png to train/cycle_s_001875.png
Saved: safety_bike_s_001590.png to train/safety_bike_s_001590.png
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Saved: minibike_s_000570.png to train/minibike_s_000570.png
Saved: bike_s_001380.png to train/bike_s_001380.png
Saved: moped_s_000236.png to train/moped_s_000236.png
Saved: motorcycle_s_000339.png to train/motorcycle_s_000339.png
Saved: safety_bike_s_001465.png to train/safety_bike_s_001465.png
Saved: motorcycle_s_001714.png to train/motorcycle_s_001714.png
Saved: minibike_s_001191.png to train/minibike_s_001191.png
Saved: cycle_s_002882.png to train/cycle_s_002882.png
Saved: bicycle_s_000822.png to train/bicycle_s_000822.png
Saved: bike_s_001827.png to train/bike_s_001827.png
Saved: ordinary_bicycle_s_000110.png to train/ordinary_bicycle_s_000110.png
Saved: minibike_s_000293.png to train/minibike_s_000293.png
Saved: motorcycle_s_000249.png to train/motorcycle_s_000249.png
Saved: ordinary_bicycle_s_000154.png to train/ordinary_bicycle_s_000154.png
Saved: motorcycle_s_001527.png to train/motorcycle_s_001527.png
Saved: motorcycle_s_001033.png to train/motorcycle_s_001033.png
Saved: motorcycle_s_001709.png to train/motorcycle_s_001709.png
Saved: bicycle_s_001348.png to train/bicycle_s_001348.png
Saved: minibike_s_002194.png to train/minibike_s_002194.png
Saved: minibike_s_001079.png to train/minibike_s_001079.png
Saved: bicycle_s_000017.png to train/bicycle_s_000017.png
Saved: minibike_s_001638.png to train/minibike_s_001638.png
Saved: motorcycle_s_001587.png to train/motorcycle_s_001587.png
Saved: motorcycle_s_000486.png to train/motorcycle_s_000486.png
Saved: minibike_s_001880.png to train/minibike_s_001880.png
Saved: dirt_bike_s_000003.png to train/dirt_bike_s_000003.png
Saved: bike_s_000336.png to train/bike_s_000336.png
Saved: minibike_s_002115.png to train/minibike_s_002115.png
Saved: motorbike_s_000449.png to train/motorbike_s_000449.png
Saved: motorcycle_s_000304.png to train/motorcycle_s_000304.png
Saved: motorcycle_s_000022.png to train/motorcycle_s_000022.png
Saved: motorcycle_s_001623.png to train/motorcycle_s_001623.png
Saved: motorbike_s_000223.png to train/motorbike_s_000223.png
Saved: motorcycle_s_000290.png to train/motorcycle_s_000290.png
Saved: safety_bicycle_s_001327.png to train/safety_bicycle_s_001327.png
Saved: bike_s_002288.png to train/bike_s_002288.png
Saved: minibike_s_000569.png to train/minibike_s_000569.png
Saved: bike_s_002047.png to train/bike_s_002047.png
Saved: motorcycle_s_002031.png to train/motorcycle_s_002031.png
Saved: bike_s_000062.png to train/bike_s_000062.png
Saved: cycle_s_000871.png to train/cycle_s_000871.png
Saved: motorcycle_s_001778.png to train/motorcycle_s_001778.png
Saved: velocipede_s_000010.png to train/velocipede_s_000010.png
Saved: motorcycle_s_001247.png to train/motorcycle_s_001247.png
Saved: motorcycle_s_000724.png to train/motorcycle_s_000724.png
Saved: velocipede_s_001637.png to train/velocipede_s_001637.png
Saved: motorcycle_s_000291.png to train/motorcycle_s_000291.png
Saved: safety_bike_s_000390.png to test/safety_bike_s_000390.png
Saved: bike_s_000658.png to test/bike_s_000658.png
Saved: velocipede_s_001744.png to test/velocipede_s_001744.png
Saved: bike_s_000643.png to test/bike_s_000643.png
Saved: ordinary_bicycle_s_000437.png to test/ordinary_bicycle_s_000437.png
Saved: motorcycle_s_000323.png to test/motorcycle_s_000323.png
Saved: safety_bike_s_001253.png to test/safety_bike_s_001253.png
Saved: cycle_s_001306.png to test/cycle_s_001306.png
Saved: velocipede_s_000863.png to test/velocipede_s_000863.png
Saved: bicycle_s_000479.png to test/bicycle_s_000479.png
Saved: minibike_s_002173.png to test/minibike_s_002173.png
Saved: motorcycle_s_000352.png to test/motorcycle_s_000352.png
Saved: bike_s_000801.png to test/bike_s_000801.png
Saved: motorbike_s_000433.png to test/motorbike_s_000433.png
Saved: bike_s_001068.png to test/bike_s_001068.png
Saved: motorcycle_s_000963.png to test/motorcycle_s_000963.png
Saved: bike_s_001852.png to test/bike_s_001852.png
Saved: motorcycle_s_000139.png to test/motorcycle_s_000139.png
Saved: motorbike_s_000465.png to test/motorbike_s_000465.png
Saved: bicycle_s_000513.png to test/bicycle_s_000513.png
Saved: cycle_s_002305.png to test/cycle_s_002305.png
Saved: motorcycle_s_000615.png to test/motorcycle_s_000615.png
Saved: moped_s_000306.png to test/moped_s_000306.png
Saved: minibike_s_000052.png to test/minibike_s_000052.png
Saved: bike_s_001342.png to test/bike_s_001342.png
Saved: cycle_s_001297.png to test/cycle_s_001297.png
Saved: motorcycle_s_001955.png to test/motorcycle_s_001955.png
Saved: cycle_s_001953.png to test/cycle_s_001953.png
Saved: motorcycle_s_000494.png to test/motorcycle_s_000494.png
Saved: motorcycle_s_001249.png to test/motorcycle_s_001249.png
Saved: velocipede_s_000292.png to test/velocipede_s_000292.png
Saved: minibike_s_001651.png to test/minibike_s_001651.png
Saved: bicycle_s_001102.png to test/bicycle_s_001102.png
Saved: safety_bike_s_001132.png to test/safety_bike_s_001132.png
Saved: moped_s_000007.png to test/moped_s_000007.png
Saved: motorcycle_s_000211.png to test/motorcycle_s_000211.png
Saved: cycle_s_000970.png to test/cycle_s_000970.png
Saved: motorcycle_s_002126.png to test/motorcycle_s_002126.png
Saved: minibike_s_001732.png to test/minibike_s_001732.png
Saved: minibike_s_001867.png to test/minibike_s_001867.png
Saved: minibike_s_002227.png to test/minibike_s_002227.png
Saved: cycle_s_000068.png to test/cycle_s_000068.png
Saved: minibike_s_000880.png to test/minibike_s_000880.png
Saved: moped_s_000033.png to test/moped_s_000033.png
Saved: bicycle_s_001107.png to test/bicycle_s_001107.png
Saved: bike_s_002116.png to test/bike_s_002116.png
Saved: bike_s_001738.png to test/bike_s_001738.png
Saved: bike_s_002009.png to test/bike_s_002009.png
Saved: minibike_s_000947.png to test/minibike_s_000947.png
Saved: bike_s_001159.png to test/bike_s_001159.png
Saved: safety_bike_s_001065.png to test/safety_bike_s_001065.png
Saved: motorbike_s_000005.png to test/motorbike_s_000005.png
Saved: minibike_s_000075.png to test/minibike_s_000075.png
Saved: motorcycle_s_001164.png to test/motorcycle_s_001164.png
Saved: motorcycle_s_000679.png to test/motorcycle_s_000679.png
Saved: motorcycle_s_001892.png to test/motorcycle_s_001892.png
Saved: bike_s_000694.png to test/bike_s_000694.png
Saved: velocipede_s_000106.png to test/velocipede_s_000106.png
Saved: velocipede_s_000001.png to test/velocipede_s_000001.png
Saved: motorcycle_s_002177.png to test/motorcycle_s_002177.png
Saved: motorbike_s_000126.png to test/motorbike_s_000126.png
Saved: safety_bicycle_s_001286.png to test/safety_bicycle_s_001286.png
Saved: motorcycle_s_001936.png to test/motorcycle_s_001936.png
Saved: safety_bike_s_001087.png to test/safety_bike_s_001087.png
Saved: cycle_s_001648.png to test/cycle_s_001648.png
Saved: minibike_s_000801.png to test/minibike_s_000801.png
Saved: motorcycle_s_000427.png to test/motorcycle_s_000427.png
Saved: minibike_s_001089.png to test/minibike_s_001089.png
Saved: motorcycle_s_000530.png to test/motorcycle_s_000530.png
Saved: velocipede_s_001232.png to test/velocipede_s_001232.png
Saved: motorcycle_s_001261.png to test/motorcycle_s_001261.png
Saved: velocipede_s_001466.png to test/velocipede_s_001466.png
Saved: motorcycle_s_001269.png to test/motorcycle_s_001269.png
Saved: bicycle_s_000776.png to test/bicycle_s_000776.png
Saved: motorcycle_s_001348.png to test/motorcycle_s_001348.png
Saved: motorcycle_s_002026.png to test/motorcycle_s_002026.png
Saved: safety_bike_s_000540.png to test/safety_bike_s_000540.png
Saved: safety_bicycle_s_000280.png to test/safety_bicycle_s_000280.png
Saved: bike_s_000131.png to test/bike_s_000131.png
Saved: cycle_s_000010.png to test/cycle_s_000010.png
Saved: minibike_s_000828.png to test/minibike_s_000828.png
Saved: minibike_s_000288.png to test/minibike_s_000288.png
Saved: velocipede_s_001355.png to test/velocipede_s_001355.png
Saved: safety_bicycle_s_001575.png to test/safety_bicycle_s_001575.png
Saved: safety_bike_s_001690.png to test/safety_bike_s_001690.png
Saved: cycle_s_001044.png to test/cycle_s_001044.png
Saved: motorcycle_s_000063.png to test/motorcycle_s_000063.png
Saved: velocipede_s_001379.png to test/velocipede_s_001379.png
Saved: minibike_s_001441.png to test/minibike_s_001441.png
Saved: motorcycle_s_000797.png to test/motorcycle_s_000797.png
Saved: velocipede_s_001790.png to test/velocipede_s_001790.png
Saved: motorbike_s_000135.png to test/motorbike_s_000135.png
Saved: cycle_s_002613.png to test/cycle_s_002613.png
Saved: motorcycle_s_001960.png to test/motorcycle_s_001960.png
Saved: minibike_s_000913.png to test/minibike_s_000913.png
Saved: motorcycle_s_000685.png to test/motorcycle_s_000685.png
Saved: ordinary_bicycle_s_000431.png to test/ordinary_bicycle_s_000431.png
Saved: minibike_s_002051.png to test/minibike_s_002051.png
Saved: motorcycle_s_000446.png to test/motorcycle_s_000446.png
Saved: velocipede_s_001699.png to test/velocipede_s_001699.png
Saved: minibike_s_000792.png to test/minibike_s_000792.png
Saved: bicycle_s_002458.png to test/bicycle_s_002458.png
Saved: minibike_s_000505.png to test/minibike_s_000505.png
Saved: minibike_s_000573.png to test/minibike_s_000573.png
Saved: velocipede_s_000369.png to test/velocipede_s_000369.png
Saved: moped_s_000064.png to test/moped_s_000064.png
Saved: minibike_s_001893.png to test/minibike_s_001893.png
Saved: motorcycle_s_000878.png to test/motorcycle_s_000878.png
Saved: motorcycle_s_000042.png to test/motorcycle_s_000042.png
Saved: motorcycle_s_000606.png to test/motorcycle_s_000606.png
Saved: minibike_s_002230.png to test/minibike_s_002230.png
Saved: motorcycle_s_001519.png to test/motorcycle_s_001519.png
Saved: motorcycle_s_000512.png to test/motorcycle_s_000512.png
Saved: cycle_s_001915.png to test/cycle_s_001915.png
Saved: safety_bike_s_000541.png to test/safety_bike_s_000541.png
Saved: safety_bike_s_000192.png to test/safety_bike_s_000192.png
Saved: bicycle_s_000030.png to test/bicycle_s_000030.png
Saved: motorcycle_s_001385.png to test/motorcycle_s_001385.png
Saved: minibike_s_000960.png to test/minibike_s_000960.png
Saved: safety_bike_s_000791.png to test/safety_bike_s_000791.png
Saved: bike_s_000071.png to test/bike_s_000071.png
Saved: motorcycle_s_000876.png to test/motorcycle_s_000876.png
Saved: ordinary_bicycle_s_000350.png to test/ordinary_bicycle_s_000350.png
Saved: motorcycle_s_000602.png to test/motorcycle_s_000602.png
Saved: motorcycle_s_000222.png to test/motorcycle_s_000222.png
Saved: minibike_s_000309.png to test/minibike_s_000309.png
Saved: motorcycle_s_001679.png to test/motorcycle_s_001679.png
Saved: minibike_s_001473.png to test/minibike_s_001473.png
Saved: velocipede_s_001633.png to test/velocipede_s_001633.png
Saved: safety_bike_s_000438.png to test/safety_bike_s_000438.png
Saved: safety_bicycle_s_000315.png to test/safety_bicycle_s_000315.png
Saved: motorcycle_s_002112.png to test/motorcycle_s_002112.png
Saved: bike_s_000163.png to test/bike_s_000163.png
Saved: bicycle_s_000059.png to test/bicycle_s_000059.png
Saved: bicycle_s_001789.png to test/bicycle_s_001789.png
Saved: safety_bike_s_000699.png to test/safety_bike_s_000699.png
Saved: motorbike_s_000324.png to test/motorbike_s_000324.png
Saved: motorcycle_s_000485.png to test/motorcycle_s_000485.png
Saved: motorcycle_s_001687.png to test/motorcycle_s_001687.png
Saved: minibike_s_000497.png to test/minibike_s_000497.png
Saved: minibike_s_001547.png to test/minibike_s_001547.png
Saved: minibike_s_000055.png to test/minibike_s_000055.png
Saved: ordinary_bicycle_s_000169.png to test/ordinary_bicycle_s_000169.png
Saved: minibike_s_000290.png to test/minibike_s_000290.png
Saved: velocipede_s_001335.png to test/velocipede_s_001335.png
Saved: bicycle_s_001804.png to test/bicycle_s_001804.png
Saved: safety_bike_s_000488.png to test/safety_bike_s_000488.png
Saved: bike_s_001216.png to test/bike_s_001216.png
Saved: motorbike_s_000121.png to test/motorbike_s_000121.png
Saved: bike_s_001073.png to test/bike_s_001073.png
Saved: safety_bicycle_s_001153.png to test/safety_bicycle_s_001153.png
Saved: motorcycle_s_000171.png to test/motorcycle_s_000171.png
Saved: velocipede_s_000041.png to test/velocipede_s_000041.png
Saved: motorcycle_s_001782.png to test/motorcycle_s_001782.png
Saved: motorbike_s_000629.png to test/motorbike_s_000629.png
Saved: bicycle_s_000977.png to test/bicycle_s_000977.png
Saved: motorcycle_s_001906.png to test/motorcycle_s_001906.png
Saved: bike_s_002208.png to test/bike_s_002208.png
Saved: minibike_s_000254.png to test/minibike_s_000254.png
Saved: safety_bicycle_s_001322.png to test/safety_bicycle_s_001322.png
Saved: bike_s_000041.png to test/bike_s_000041.png
Saved: bicycle_s_000031.png to test/bicycle_s_000031.png
Saved: bicycle_s_000779.png to test/bicycle_s_000779.png
Saved: velocipede_s_001277.png to test/velocipede_s_001277.png
Saved: safety_bike_s_001088.png to test/safety_bike_s_001088.png
Saved: motorcycle_s_001971.png to test/motorcycle_s_001971.png
Saved: bike_s_001784.png to test/bike_s_001784.png
Saved: motorcycle_s_000825.png to test/motorcycle_s_000825.png
Saved: dirt_bike_s_000001.png to test/dirt_bike_s_000001.png
Saved: cycle_s_001214.png to test/cycle_s_001214.png
Saved: cycle_s_002964.png to test/cycle_s_002964.png
Saved: ordinary_bicycle_s_000284.png to test/ordinary_bicycle_s_000284.png
Saved: bicycle_s_002735.png to test/bicycle_s_002735.png
Saved: motorcycle_s_000450.png to test/motorcycle_s_000450.png
Saved: motorcycle_s_000049.png to test/motorcycle_s_000049.png
Saved: motorcycle_s_000866.png to test/motorcycle_s_000866.png
Saved: velocipede_s_001201.png to test/velocipede_s_001201.png
Saved: bike_s_000457.png to test/bike_s_000457.png
Saved: bike_s_000330.png to test/bike_s_000330.png
Saved: minibike_s_001605.png to test/minibike_s_001605.png
Saved: minibike_s_000398.png to test/minibike_s_000398.png
Saved: motorcycle_s_000007.png to test/motorcycle_s_000007.png
Saved: motorcycle_s_000040.png to test/motorcycle_s_000040.png
Saved: motorcycle_s_000739.png to test/motorcycle_s_000739.png
Saved: motorbike_s_000333.png to test/motorbike_s_000333.png
Saved: safety_bike_s_001481.png to test/safety_bike_s_001481.png
Saved: ordinary_bicycle_s_000158.png to test/ordinary_bicycle_s_000158.png
Saved: motorcycle_s_001508.png to test/motorcycle_s_001508.png
Saved: motorcycle_s_000060.png to test/motorcycle_s_000060.png
Saved: cycle_s_002661.png to test/cycle_s_002661.png
Saved: bicycle_s_001047.png to test/bicycle_s_001047.png
Saved: bicycle_s_000777.png to test/bicycle_s_000777.png
Saved: safety_bicycle_s_000235.png to test/safety_bicycle_s_000235.png
Saved: bike_s_000487.png to test/bike_s_000487.png
Saved: ordinary_bicycle_s_000113.png to test/ordinary_bicycle_s_000113.png
Saved: trail_bike_s_000073.png to test/trail_bike_s_000073.png
Saved: bike_s_000941.png to test/bike_s_000941.png
Saved: motorcycle_s_000141.png to test/motorcycle_s_000141.png
Saved: ordinary_bicycle_s_000105.png to test/ordinary_bicycle_s_000105.png
Saved: bicycle_s_001218.png to test/bicycle_s_001218.png

3. Load the data¶

Now we can load the data into S3.

Using the sagemaker SDK grab the current region, execution role, and bucket.

In [40]:
import sagemaker
#from sagemaker import image_uris

bucket= "sconesmodel" ## TODO: fill in
print("Default Bucket: {}".format(bucket))

region = "us-east-1" ## TODO: fill in
print("AWS Region: {}".format(region))

role = "arn:aws:iam::342059937233:role/service-role/AmazonSageMaker-ExecutionRole-20230823T174610" ## TODO: fill in
print("RoleArn: {}".format(role))

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml
Default Bucket: sconesmodel
AWS Region: us-east-1
RoleArn: arn:aws:iam::342059937233:role/service-role/AmazonSageMaker-ExecutionRole-20230823T174610

With this data we can easily sync your data up into S3!

In [41]:
import os

os.environ["DEFAULT_S3_BUCKET"] = bucket
!aws s3 sync ./train s3://${DEFAULT_S3_BUCKET}/train/
!aws s3 sync ./test s3://${DEFAULT_S3_BUCKET}/test/

upload: train/bicycle_s_000017.png to s3://sconesmodel/train/bicycle_s_000017.png   
upload: train/bicycle_s_000021.png to s3://sconesmodel/train/bicycle_s_000021.png    
upload: train/bicycle_s_000124.png to s3://sconesmodel/train/bicycle_s_000124.png    
upload: train/bicycle_s_000038.png to s3://sconesmodel/train/bicycle_s_000038.png     
upload: train/bicycle_s_000147.png to s3://sconesmodel/train/bicycle_s_000147.png     
upload: train/bicycle_s_000149.png to s3://sconesmodel/train/bicycle_s_000149.png     
upload: train/bicycle_s_000035.png to s3://sconesmodel/train/bicycle_s_000035.png     
upload: train/bicycle_s_000071.png to s3://sconesmodel/train/bicycle_s_000071.png      
upload: train/bicycle_s_000137.png to s3://sconesmodel/train/bicycle_s_000137.png      
upload: train/bicycle_s_000180.png to s3://sconesmodel/train/bicycle_s_000180.png      
upload: train/bicycle_s_000051.png to s3://sconesmodel/train/bicycle_s_000051.png      
upload: train/bicycle_s_000159.png to s3://sconesmodel/train/bicycle_s_000159.png      
upload: train/bicycle_s_000099.png to s3://sconesmodel/train/bicycle_s_000099.png      
upload: train/bicycle_s_000039.png to s3://sconesmodel/train/bicycle_s_000039.png      
upload: train/bicycle_s_000156.png to s3://sconesmodel/train/bicycle_s_000156.png      
upload: train/bicycle_s_000066.png to s3://sconesmodel/train/bicycle_s_000066.png      
upload: train/bicycle_s_000043.png to s3://sconesmodel/train/bicycle_s_000043.png      
upload: train/bicycle_s_000235.png to s3://sconesmodel/train/bicycle_s_000235.png      
upload: train/bicycle_s_000369.png to s3://sconesmodel/train/bicycle_s_000369.png      
upload: train/bicycle_s_000279.png to s3://sconesmodel/train/bicycle_s_000279.png      
upload: train/bicycle_s_000231.png to s3://sconesmodel/train/bicycle_s_000231.png      
upload: train/bicycle_s_000371.png to s3://sconesmodel/train/bicycle_s_000371.png      
upload: train/bicycle_s_000392.png to s3://sconesmodel/train/bicycle_s_000392.png      
upload: train/bicycle_s_000243.png to s3://sconesmodel/train/bicycle_s_000243.png      
upload: train/bicycle_s_000396.png to s3://sconesmodel/train/bicycle_s_000396.png      
upload: train/bicycle_s_000282.png to s3://sconesmodel/train/bicycle_s_000282.png      
upload: train/bicycle_s_000399.png to s3://sconesmodel/train/bicycle_s_000399.png      
upload: train/bicycle_s_000408.png to s3://sconesmodel/train/bicycle_s_000408.png      
upload: train/bicycle_s_000435.png to s3://sconesmodel/train/bicycle_s_000435.png      
upload: train/bicycle_s_000437.png to s3://sconesmodel/train/bicycle_s_000437.png      
upload: train/bicycle_s_000463.png to s3://sconesmodel/train/bicycle_s_000463.png      
upload: train/bicycle_s_000467.png to s3://sconesmodel/train/bicycle_s_000467.png      
upload: train/bicycle_s_000522.png to s3://sconesmodel/train/bicycle_s_000522.png      
upload: train/bicycle_s_000491.png to s3://sconesmodel/train/bicycle_s_000491.png      
upload: train/bicycle_s_000314.png to s3://sconesmodel/train/bicycle_s_000314.png      
upload: train/bicycle_s_000561.png to s3://sconesmodel/train/bicycle_s_000561.png      
upload: train/bicycle_s_000536.png to s3://sconesmodel/train/bicycle_s_000536.png      
upload: train/bicycle_s_000668.png to s3://sconesmodel/train/bicycle_s_000668.png       
upload: train/bicycle_s_000537.png to s3://sconesmodel/train/bicycle_s_000537.png       
upload: train/bicycle_s_000753.png to s3://sconesmodel/train/bicycle_s_000753.png       
upload: train/bicycle_s_000723.png to s3://sconesmodel/train/bicycle_s_000723.png       
upload: train/bicycle_s_000569.png to s3://sconesmodel/train/bicycle_s_000569.png       
upload: train/bicycle_s_000774.png to s3://sconesmodel/train/bicycle_s_000774.png       
upload: train/bicycle_s_000778.png to s3://sconesmodel/train/bicycle_s_000778.png       
upload: train/bicycle_s_000759.png to s3://sconesmodel/train/bicycle_s_000759.png       
upload: train/bicycle_s_000822.png to s3://sconesmodel/train/bicycle_s_000822.png       
upload: train/bicycle_s_000546.png to s3://sconesmodel/train/bicycle_s_000546.png       
upload: train/bicycle_s_000775.png to s3://sconesmodel/train/bicycle_s_000775.png       
upload: train/bicycle_s_000782.png to s3://sconesmodel/train/bicycle_s_000782.png       
upload: train/bicycle_s_000829.png to s3://sconesmodel/train/bicycle_s_000829.png       
upload: train/bicycle_s_000781.png to s3://sconesmodel/train/bicycle_s_000781.png       
upload: train/bicycle_s_000861.png to s3://sconesmodel/train/bicycle_s_000861.png       
upload: train/bicycle_s_000986.png to s3://sconesmodel/train/bicycle_s_000986.png       
upload: train/bicycle_s_000785.png to s3://sconesmodel/train/bicycle_s_000785.png       
upload: train/bicycle_s_000667.png to s3://sconesmodel/train/bicycle_s_000667.png       
upload: train/bicycle_s_000996.png to s3://sconesmodel/train/bicycle_s_000996.png       
upload: train/bicycle_s_000951.png to s3://sconesmodel/train/bicycle_s_000951.png       
upload: train/bicycle_s_001002.png to s3://sconesmodel/train/bicycle_s_001002.png       
upload: train/bicycle_s_001111.png to s3://sconesmodel/train/bicycle_s_001111.png       
upload: train/bicycle_s_001168.png to s3://sconesmodel/train/bicycle_s_001168.png       
upload: train/bicycle_s_001174.png to s3://sconesmodel/train/bicycle_s_001174.png       
upload: train/bicycle_s_001247.png to s3://sconesmodel/train/bicycle_s_001247.png       
upload: train/bicycle_s_001228.png to s3://sconesmodel/train/bicycle_s_001228.png       
upload: train/bicycle_s_000978.png to s3://sconesmodel/train/bicycle_s_000978.png       
upload: train/bicycle_s_001245.png to s3://sconesmodel/train/bicycle_s_001245.png       
upload: train/bicycle_s_001205.png to s3://sconesmodel/train/bicycle_s_001205.png       
upload: train/bicycle_s_001338.png to s3://sconesmodel/train/bicycle_s_001338.png       
upload: train/bicycle_s_001203.png to s3://sconesmodel/train/bicycle_s_001203.png       
upload: train/bicycle_s_001388.png to s3://sconesmodel/train/bicycle_s_001388.png       
upload: train/bicycle_s_001348.png to s3://sconesmodel/train/bicycle_s_001348.png       
upload: train/bicycle_s_001411.png to s3://sconesmodel/train/bicycle_s_001411.png       
upload: train/bicycle_s_001409.png to s3://sconesmodel/train/bicycle_s_001409.png       
upload: train/bicycle_s_001452.png to s3://sconesmodel/train/bicycle_s_001452.png       
upload: train/bicycle_s_001554.png to s3://sconesmodel/train/bicycle_s_001554.png       
upload: train/bicycle_s_001642.png to s3://sconesmodel/train/bicycle_s_001642.png       
upload: train/bicycle_s_001448.png to s3://sconesmodel/train/bicycle_s_001448.png       
upload: train/bicycle_s_001583.png to s3://sconesmodel/train/bicycle_s_001583.png       
upload: train/bicycle_s_001681.png to s3://sconesmodel/train/bicycle_s_001681.png        
upload: train/bicycle_s_001569.png to s3://sconesmodel/train/bicycle_s_001569.png        
upload: train/bicycle_s_001402.png to s3://sconesmodel/train/bicycle_s_001402.png        
upload: train/bicycle_s_001470.png to s3://sconesmodel/train/bicycle_s_001470.png        
upload: train/bicycle_s_001687.png to s3://sconesmodel/train/bicycle_s_001687.png        
upload: train/bicycle_s_001679.png to s3://sconesmodel/train/bicycle_s_001679.png        
upload: train/bicycle_s_001663.png to s3://sconesmodel/train/bicycle_s_001663.png        
upload: train/bicycle_s_001673.png to s3://sconesmodel/train/bicycle_s_001673.png        
upload: train/bicycle_s_001757.png to s3://sconesmodel/train/bicycle_s_001757.png        
upload: train/bicycle_s_001693.png to s3://sconesmodel/train/bicycle_s_001693.png        
upload: train/bicycle_s_001748.png to s3://sconesmodel/train/bicycle_s_001748.png        
upload: train/bicycle_s_001814.png to s3://sconesmodel/train/bicycle_s_001814.png        
upload: train/bicycle_s_002012.png to s3://sconesmodel/train/bicycle_s_002012.png        
upload: train/bicycle_s_001904.png to s3://sconesmodel/train/bicycle_s_001904.png        
upload: train/bicycle_s_002005.png to s3://sconesmodel/train/bicycle_s_002005.png        
upload: train/bicycle_s_001688.png to s3://sconesmodel/train/bicycle_s_001688.png        
upload: train/bicycle_s_001928.png to s3://sconesmodel/train/bicycle_s_001928.png        
upload: train/bicycle_s_002049.png to s3://sconesmodel/train/bicycle_s_002049.png        
upload: train/bicycle_s_002029.png to s3://sconesmodel/train/bicycle_s_002029.png        
upload: train/bicycle_s_001956.png to s3://sconesmodel/train/bicycle_s_001956.png        
upload: train/bicycle_s_002237.png to s3://sconesmodel/train/bicycle_s_002237.png        
upload: train/bicycle_s_002132.png to s3://sconesmodel/train/bicycle_s_002132.png        
upload: train/bicycle_s_002222.png to s3://sconesmodel/train/bicycle_s_002222.png        
upload: train/bicycle_s_002218.png to s3://sconesmodel/train/bicycle_s_002218.png        
upload: train/bicycle_s_002100.png to s3://sconesmodel/train/bicycle_s_002100.png        
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upload: test/test_image_9934.png to s3://sconesmodel/test/test_image_9934.png
upload: test/test_image_994.png to s3://sconesmodel/test/test_image_994.png
upload: test/test_image_9936.png to s3://sconesmodel/test/test_image_9936.png
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upload: test/test_image_9938.png to s3://sconesmodel/test/test_image_9938.png
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upload: test/test_image_9948.png to s3://sconesmodel/test/test_image_9948.png
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upload: test/test_image_9945.png to s3://sconesmodel/test/test_image_9945.png
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upload: test/test_image_9999.png to s3://sconesmodel/test/test_image_9999.png
upload: test/velocipede_s_000041.png to s3://sconesmodel/test/velocipede_s_000041.png
upload: test/velocipede_s_000001.png to s3://sconesmodel/test/velocipede_s_000001.png
upload: test/test_image_9991.png to s3://sconesmodel/test/test_image_9991.png
upload: test/velocipede_s_000106.png to s3://sconesmodel/test/velocipede_s_000106.png
upload: test/velocipede_s_000863.png to s3://sconesmodel/test/velocipede_s_000863.png
upload: test/velocipede_s_000369.png to s3://sconesmodel/test/velocipede_s_000369.png
upload: test/trail_bike_s_000073.png to s3://sconesmodel/test/trail_bike_s_000073.png
upload: test/velocipede_s_001232.png to s3://sconesmodel/test/velocipede_s_001232.png
upload: test/velocipede_s_001379.png to s3://sconesmodel/test/velocipede_s_001379.png
upload: test/test_image_9997.png to s3://sconesmodel/test/test_image_9997.png
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upload: test/velocipede_s_001335.png to s3://sconesmodel/test/velocipede_s_001335.png
upload: test/velocipede_s_001466.png to s3://sconesmodel/test/velocipede_s_001466.png
upload: test/velocipede_s_000292.png to s3://sconesmodel/test/velocipede_s_000292.png
upload: test/velocipede_s_001201.png to s3://sconesmodel/test/velocipede_s_001201.png
upload: test/velocipede_s_001633.png to s3://sconesmodel/test/velocipede_s_001633.png
upload: test/velocipede_s_001699.png to s3://sconesmodel/test/velocipede_s_001699.png
upload: test/velocipede_s_001790.png to s3://sconesmodel/test/velocipede_s_001790.png
upload: test/velocipede_s_001744.png to s3://sconesmodel/test/velocipede_s_001744.png

And that's it! You can check the bucket and verify that the items were uploaded.

Model Training¶

For Image Classification, Sagemaker also expects metadata e.g. in the form of TSV files with labels and filepaths. We can generate these using our Pandas DataFrames from earlier:

In [42]:
def to_metadata_file(df, prefix):
    df["s3_path"] = df["filenames"]
    df["labels"] = df["labels"].apply(lambda x: 0 if x==8 else 1)
    return df[["row", "labels", "s3_path"]].to_csv(
        f"{prefix}.lst", sep="\t", index=False, header=False
    )
    
to_metadata_file(df_train.copy(), "train")
to_metadata_file(df_test.copy(), "test")

We can also upload our manifest files:

In [43]:
import boto3

# Upload files
boto3.Session().resource('s3').Bucket(
    bucket).Object('train.lst').upload_file('./train.lst')
boto3.Session().resource('s3').Bucket(
    bucket).Object('test.lst').upload_file('./test.lst')

Using the bucket and region info we can get the latest prebuilt container to run our training job, and define an output location on our s3 bucket for the model. Use the image_uris function from the SageMaker SDK to retrieve the latest image-classification image below:

In [44]:
# Use the image_uris function to retrieve the latest 'image-classification' image 
import sagemaker
from sagemaker.image_uris import retrieve

algo_image = retrieve('image-classification',region, version="1") ## TODO: fill in
s3_output_location = f"s3://{bucket}/models/image_model"
print(s3_output_location)

s3://sconesmodel/models/image_model

We're ready to create an estimator! Create an estimator img_classifier_model that uses one instance of ml.p3.2xlarge. Ensure that y ou use the output location we defined above - we'll be referring to that later!

In [50]:
img_classifier_model=sagemaker.estimator.Estimator(
    image_uri=algo_image,role=role,
    instance_type="ml.p3.2xlarge",instance_count=1,
    output_path=s3_output_location,
    framework_version="1.0",
    #code_location="s3://your-bucket-name/training-code",
    base_job_name="img-classification-job",
    sagemaker_session=sagemaker.Session()
    ## TODO: define your estimator options    
)

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml

We can also set a few key hyperparameters and define the inputs for our model:

In [51]:
img_classifier_model.set_hyperparameters(
    image_shape="3,32,32", # TODO: Fill in
    num_classes=2, # TODO: Fill in
    num_training_samples=len(df_train),
    augmentation_type='crop_color_transform',
    early_stopping=True,
    early_stopping_min_epochs=10,
    mini_batch_size=32,
    epochs=30,
    optimizer='sgd',
    # TODO: fill in
)

The image-classification image uses four input channels with very specific input parameters. For convenience, we've provided them below:

In [52]:
from sagemaker.debugger import Rule, rule_configs
from sagemaker.session import TrainingInput
model_inputs = {
        "train": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/train/",
            content_type="application/x-image"
        ),
        "validation": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/test/",
            content_type="application/x-image"
        ),
        "train_lst": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/train.lst",
            content_type="application/x-image"
        ),
        "validation_lst": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/test.lst",
            content_type="application/x-image"
        )
}

Great, now we can train the model using the model_inputs. In the cell below, call the fit method on our model,:

In [53]:
## TODO: train your model
img_classifier_model.fit(inputs=model_inputs)

INFO:sagemaker:Creating training-job with name: img-classification-job-2023-09-19-09-17-50-270

2023-09-19 09:17:50 Starting - Starting the training job...
2023-09-19 09:18:15 Starting - Preparing the instances for training.........
2023-09-19 09:19:25 Downloading - Downloading input data......
2023-09-19 09:20:30 Training - Downloading the training image...............
2023-09-19 09:23:11 Training - Training image download completed. Training in progress....Docker entrypoint called with argument(s): train
Running default environment configuration script
Nvidia gpu devices, drivers and cuda toolkit versions (only available on hosts with GPU):
Tue Sep 19 09:23:35 2023       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.54.03              Driver Version: 535.54.03    CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla V100-SXM2-16GB           On  | 00000000:00:1E.0 Off |                    0 |
| N/A   31C    P0              23W / 300W |      0MiB / 16384MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+
Checking for nvidia driver and cuda compatibility.
CUDA Compatibility driver provided.
Proceeding with compatibility check between driver, cuda-toolkit and cuda-compat.
Detected cuda-toolkit version: 11.1.
Detected cuda-compat version: 455.32.00.
Detected Nvidia driver version: 535.54.03.
Nvidia driver compatible with cuda-toolkit. Disabling cuda-compat.
[09/19/2023 09:23:39 INFO 140460310353728] Reading default configuration from /opt/amazon/lib/python3.7/site-packages/image_classification/default-input.json: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': 30, 'learning_rate': 0.1, 'lr_scheduler_factor': 0.1, 'optimizer': 'sgd', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': 32, 'image_shape': '3,224,224', 'precision_dtype': 'float32'}
[09/19/2023 09:23:39 INFO 140460310353728] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'augmentation_type': 'crop_color_transform', 'early_stopping': 'True', 'early_stopping_min_epochs': '10', 'epochs': '30', 'image_shape': '3,32,32', 'mini_batch_size': '32', 'num_classes': '2', 'num_training_samples': '1000', 'optimizer': 'sgd'}
[09/19/2023 09:23:39 INFO 140460310353728] Final configuration: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': '30', 'learning_rate': 0.1, 'lr_scheduler_factor': 0.1, 'optimizer': 'sgd', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': '32', 'image_shape': '3,32,32', 'precision_dtype': 'float32', 'augmentation_type': 'crop_color_transform', 'early_stopping': 'True', 'early_stopping_min_epochs': '10', 'num_classes': '2', 'num_training_samples': '1000'}
[09/19/2023 09:23:39 INFO 140460310353728] Searching for .lst files in /opt/ml/input/data/train_lst.
[09/19/2023 09:23:39 INFO 140460310353728] Creating record files for train.lst
[09/19/2023 09:23:39 INFO 140460310353728] Done creating record files...
[09/19/2023 09:23:39 INFO 140460310353728] Searching for .lst files in /opt/ml/input/data/validation_lst.
[09/19/2023 09:23:39 INFO 140460310353728] Creating record files for test.lst
[09/19/2023 09:23:39 INFO 140460310353728] Done creating record files...
[09/19/2023 09:23:39 INFO 140460310353728] use_pretrained_model: 0
[09/19/2023 09:23:39 INFO 140460310353728] multi_label: 0
[09/19/2023 09:23:39 INFO 140460310353728] Performing random weight initialization
[09/19/2023 09:23:39 INFO 140460310353728] ---- Parameters ----
[09/19/2023 09:23:39 INFO 140460310353728] num_layers: 152
[09/19/2023 09:23:39 INFO 140460310353728] data type: <class 'numpy.float32'>
[09/19/2023 09:23:39 INFO 140460310353728] epochs: 30
[09/19/2023 09:23:39 INFO 140460310353728] optimizer: sgd
[09/19/2023 09:23:39 INFO 140460310353728] momentum: 0.9
[09/19/2023 09:23:39 INFO 140460310353728] weight_decay: 0.0001
[09/19/2023 09:23:39 INFO 140460310353728] learning_rate: 0.1
[09/19/2023 09:23:39 INFO 140460310353728] num_training_samples: 1000
[09/19/2023 09:23:39 INFO 140460310353728] mini_batch_size: 32
[09/19/2023 09:23:39 INFO 140460310353728] image_shape: 3,32,32
[09/19/2023 09:23:39 INFO 140460310353728] num_classes: 2
[09/19/2023 09:23:39 INFO 140460310353728] augmentation_type: crop_color_transform
[09/19/2023 09:23:39 INFO 140460310353728] kv_store: device
[09/19/2023 09:23:39 INFO 140460310353728] checkpoint_frequency not set, will store the best model
[09/19/2023 09:23:39 INFO 140460310353728] Using early stopping for training
[09/19/2023 09:23:39 INFO 140460310353728] Early stopping minimum epochs: 10
[09/19/2023 09:23:39 INFO 140460310353728] Early stopping patience: 10
[09/19/2023 09:23:39 INFO 140460310353728] Early stopping tolerance: 0.01
[09/19/2023 09:23:39 INFO 140460310353728] --------------------
[09/19/2023 09:23:39 INFO 140460310353728] Setting number of threads: 7
[09:23:43] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_11.1.x.230.0/AL2_x86_64/generic-flavor/src/src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
[09/19/2023 09:23:48 INFO 140460310353728] Epoch[0] Batch [20]#011Speed: 120.525 samples/sec#011accuracy=0.541667
[09/19/2023 09:23:50 INFO 140460310353728] Epoch[0] Train-accuracy=0.541331
[09/19/2023 09:23:50 INFO 140460310353728] Epoch[0] Time cost=6.991
[09/19/2023 09:23:50 INFO 140460310353728] Epoch[0] Validation-accuracy=0.552083
[09/19/2023 09:23:51 INFO 140460310353728] Storing the best model with validation accuracy: 0.552083
[09/19/2023 09:23:51 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0001.params"
[09/19/2023 09:23:54 INFO 140460310353728] Epoch[1] Batch [20]#011Speed: 179.372 samples/sec#011accuracy=0.528274
[09/19/2023 09:23:56 INFO 140460310353728] Epoch[1] Train-accuracy=0.542339
[09/19/2023 09:23:56 INFO 140460310353728] Epoch[1] Time cost=5.251
[09/19/2023 09:23:57 INFO 140460310353728] Epoch[1] Validation-accuracy=0.583333
[09/19/2023 09:23:57 INFO 140460310353728] Storing the best model with validation accuracy: 0.583333
[09/19/2023 09:23:57 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0002.params"
[09/19/2023 09:24:01 INFO 140460310353728] Epoch[2] Batch [20]#011Speed: 184.729 samples/sec#011accuracy=0.556548
[09/19/2023 09:24:03 INFO 140460310353728] Epoch[2] Train-accuracy=0.556452
[09/19/2023 09:24:03 INFO 140460310353728] Epoch[2] Time cost=5.218
[09/19/2023 09:24:03 INFO 140460310353728] Epoch[2] Validation-accuracy=0.708333
[09/19/2023 09:24:03 INFO 140460310353728] Storing the best model with validation accuracy: 0.708333
[09/19/2023 09:24:04 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0003.params"
[09/19/2023 09:24:07 INFO 140460310353728] Epoch[3] Batch [20]#011Speed: 185.875 samples/sec#011accuracy=0.596726
[09/19/2023 09:24:09 INFO 140460310353728] Epoch[3] Train-accuracy=0.639113
[09/19/2023 09:24:09 INFO 140460310353728] Epoch[3] Time cost=5.143
[09/19/2023 09:24:09 INFO 140460310353728] Epoch[3] Validation-accuracy=0.741071
[09/19/2023 09:24:10 INFO 140460310353728] Storing the best model with validation accuracy: 0.741071
[09/19/2023 09:24:10 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0004.params"
[09/19/2023 09:24:14 INFO 140460310353728] Epoch[4] Batch [20]#011Speed: 183.817 samples/sec#011accuracy=0.675595
[09/19/2023 09:24:15 INFO 140460310353728] Epoch[4] Train-accuracy=0.677419
[09/19/2023 09:24:15 INFO 140460310353728] Epoch[4] Time cost=5.183
[09/19/2023 09:24:16 INFO 140460310353728] Epoch[4] Validation-accuracy=0.697917
[09/19/2023 09:24:20 INFO 140460310353728] Epoch[5] Batch [20]#011Speed: 185.694 samples/sec#011accuracy=0.702381
[09/19/2023 09:24:21 INFO 140460310353728] Epoch[5] Train-accuracy=0.709677
[09/19/2023 09:24:21 INFO 140460310353728] Epoch[5] Time cost=5.143
[09/19/2023 09:24:22 INFO 140460310353728] Epoch[5] Validation-accuracy=0.750000
[09/19/2023 09:24:22 INFO 140460310353728] Storing the best model with validation accuracy: 0.750000
[09/19/2023 09:24:23 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0006.params"
[09/19/2023 09:24:26 INFO 140460310353728] Epoch[6] Batch [20]#011Speed: 185.370 samples/sec#011accuracy=0.732143
[09/19/2023 09:24:28 INFO 140460310353728] Epoch[6] Train-accuracy=0.729839
[09/19/2023 09:24:28 INFO 140460310353728] Epoch[6] Time cost=5.142
[09/19/2023 09:24:28 INFO 140460310353728] Epoch[6] Validation-accuracy=0.729167
[09/19/2023 09:24:32 INFO 140460310353728] Epoch[7] Batch [20]#011Speed: 186.294 samples/sec#011accuracy=0.730655
[09/19/2023 09:24:34 INFO 140460310353728] Epoch[7] Train-accuracy=0.737903
[09/19/2023 09:24:34 INFO 140460310353728] Epoch[7] Time cost=5.129
[09/19/2023 09:24:34 INFO 140460310353728] Epoch[7] Validation-accuracy=0.799107
[09/19/2023 09:24:35 INFO 140460310353728] Storing the best model with validation accuracy: 0.799107
[09/19/2023 09:24:35 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0008.params"
[09/19/2023 09:24:39 INFO 140460310353728] Epoch[8] Batch [20]#011Speed: 185.351 samples/sec#011accuracy=0.745536
[09/19/2023 09:24:40 INFO 140460310353728] Epoch[8] Train-accuracy=0.755040
[09/19/2023 09:24:40 INFO 140460310353728] Epoch[8] Time cost=5.156
[09/19/2023 09:24:41 INFO 140460310353728] Epoch[8] Validation-accuracy=0.755208
[09/19/2023 09:24:45 INFO 140460310353728] Epoch[9] Batch [20]#011Speed: 184.690 samples/sec#011accuracy=0.761905
[09/19/2023 09:24:46 INFO 140460310353728] Epoch[9] Train-accuracy=0.764113
[09/19/2023 09:24:46 INFO 140460310353728] Epoch[9] Time cost=5.167
[09/19/2023 09:24:47 INFO 140460310353728] Epoch[9] Validation-accuracy=0.817708
[09/19/2023 09:24:47 INFO 140460310353728] Storing the best model with validation accuracy: 0.817708
[09/19/2023 09:24:48 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0010.params"
[09/19/2023 09:24:51 INFO 140460310353728] Epoch[10] Batch [20]#011Speed: 185.994 samples/sec#011accuracy=0.729167
[09/19/2023 09:24:53 INFO 140460310353728] Epoch[10] Train-accuracy=0.745968
[09/19/2023 09:24:53 INFO 140460310353728] Epoch[10] Time cost=5.152
[09/19/2023 09:24:53 INFO 140460310353728] Epoch[10] Validation-accuracy=0.838542
[09/19/2023 09:24:54 INFO 140460310353728] Storing the best model with validation accuracy: 0.838542
[09/19/2023 09:24:54 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0011.params"
[09/19/2023 09:24:57 INFO 140460310353728] Epoch[11] Batch [20]#011Speed: 185.480 samples/sec#011accuracy=0.757440
[09/19/2023 09:24:59 INFO 140460310353728] Epoch[11] Train-accuracy=0.757056
[09/19/2023 09:24:59 INFO 140460310353728] Epoch[11] Time cost=5.135
[09/19/2023 09:25:00 INFO 140460310353728] Epoch[11] Validation-accuracy=0.794643
[09/19/2023 09:25:04 INFO 140460310353728] Epoch[12] Batch [20]#011Speed: 178.855 samples/sec#011accuracy=0.785714
[09/19/2023 09:25:05 INFO 140460310353728] Epoch[12] Train-accuracy=0.774194
[09/19/2023 09:25:05 INFO 140460310353728] Epoch[12] Time cost=5.266
[09/19/2023 09:25:06 INFO 140460310353728] Epoch[12] Validation-accuracy=0.729167
[09/19/2023 09:25:10 INFO 140460310353728] Epoch[13] Batch [20]#011Speed: 187.252 samples/sec#011accuracy=0.745536
[09/19/2023 09:25:12 INFO 140460310353728] Epoch[13] Train-accuracy=0.764113
[09/19/2023 09:25:12 INFO 140460310353728] Epoch[13] Time cost=5.121
[09/19/2023 09:25:12 INFO 140460310353728] Epoch[13] Validation-accuracy=0.838542
[09/19/2023 09:25:16 INFO 140460310353728] Epoch[14] Batch [20]#011Speed: 186.802 samples/sec#011accuracy=0.805060
[09/19/2023 09:25:18 INFO 140460310353728] Epoch[14] Train-accuracy=0.793347
[09/19/2023 09:25:18 INFO 140460310353728] Epoch[14] Time cost=5.116
[09/19/2023 09:25:18 INFO 140460310353728] Epoch[14] Validation-accuracy=0.776042
[09/19/2023 09:25:22 INFO 140460310353728] Epoch[15] Batch [20]#011Speed: 185.322 samples/sec#011accuracy=0.775298
[09/19/2023 09:25:24 INFO 140460310353728] Epoch[15] Train-accuracy=0.776210
[09/19/2023 09:25:24 INFO 140460310353728] Epoch[15] Time cost=5.173
[09/19/2023 09:25:24 INFO 140460310353728] Epoch[15] Validation-accuracy=0.776786
[09/19/2023 09:25:28 INFO 140460310353728] Epoch[16] Batch [20]#011Speed: 182.446 samples/sec#011accuracy=0.773810
[09/19/2023 09:25:30 INFO 140460310353728] Epoch[16] Train-accuracy=0.768145
[09/19/2023 09:25:30 INFO 140460310353728] Epoch[16] Time cost=5.224
[09/19/2023 09:25:31 INFO 140460310353728] Epoch[16] Validation-accuracy=0.843750
[09/19/2023 09:25:31 INFO 140460310353728] Storing the best model with validation accuracy: 0.843750
[09/19/2023 09:25:31 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0017.params"
[09/19/2023 09:25:35 INFO 140460310353728] Epoch[17] Batch [20]#011Speed: 184.596 samples/sec#011accuracy=0.808036
[09/19/2023 09:25:36 INFO 140460310353728] Epoch[17] Train-accuracy=0.804435
[09/19/2023 09:25:36 INFO 140460310353728] Epoch[17] Time cost=5.149
[09/19/2023 09:25:37 INFO 140460310353728] Epoch[17] Validation-accuracy=0.817708
[09/19/2023 09:25:41 INFO 140460310353728] Epoch[18] Batch [20]#011Speed: 185.341 samples/sec#011accuracy=0.815476
[09/19/2023 09:25:43 INFO 140460310353728] Epoch[18] Train-accuracy=0.810484
[09/19/2023 09:25:43 INFO 140460310353728] Epoch[18] Time cost=5.118
[09/19/2023 09:25:43 INFO 140460310353728] Epoch[18] Validation-accuracy=0.833333
[09/19/2023 09:25:47 INFO 140460310353728] Epoch[19] Batch [20]#011Speed: 185.390 samples/sec#011accuracy=0.815476
[09/19/2023 09:25:49 INFO 140460310353728] Epoch[19] Train-accuracy=0.811492
[09/19/2023 09:25:49 INFO 140460310353728] Epoch[19] Time cost=5.129
[09/19/2023 09:25:49 INFO 140460310353728] Epoch[19] Validation-accuracy=0.825893
[09/19/2023 09:25:53 INFO 140460310353728] Epoch[20] Batch [20]#011Speed: 185.701 samples/sec#011accuracy=0.800595
[09/19/2023 09:25:55 INFO 140460310353728] Epoch[20] Train-accuracy=0.794355
[09/19/2023 09:25:55 INFO 140460310353728] Epoch[20] Time cost=5.130
[09/19/2023 09:25:55 INFO 140460310353728] Epoch[20] Validation-accuracy=0.859375
[09/19/2023 09:25:56 INFO 140460310353728] Storing the best model with validation accuracy: 0.859375
[09/19/2023 09:25:56 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0021.params"

2023-09-19 09:26:57 Uploading - Uploading generated training model[09/19/2023 09:25:59 INFO 140460310353728] Epoch[21] Batch [20]#011Speed: 186.456 samples/sec#011accuracy=0.825893
[09/19/2023 09:26:01 INFO 140460310353728] Epoch[21] Train-accuracy=0.836694
[09/19/2023 09:26:01 INFO 140460310353728] Epoch[21] Time cost=5.134
[09/19/2023 09:26:02 INFO 140460310353728] Epoch[21] Validation-accuracy=0.859375
[09/19/2023 09:26:06 INFO 140460310353728] Epoch[22] Batch [20]#011Speed: 182.179 samples/sec#011accuracy=0.805060
[09/19/2023 09:26:07 INFO 140460310353728] Epoch[22] Train-accuracy=0.805444
[09/19/2023 09:26:07 INFO 140460310353728] Epoch[22] Time cost=5.199
[09/19/2023 09:26:08 INFO 140460310353728] Epoch[22] Validation-accuracy=0.786458
[09/19/2023 09:26:12 INFO 140460310353728] Epoch[23] Batch [20]#011Speed: 186.530 samples/sec#011accuracy=0.825893
[09/19/2023 09:26:13 INFO 140460310353728] Epoch[23] Train-accuracy=0.823589
[09/19/2023 09:26:13 INFO 140460310353728] Epoch[23] Time cost=5.112
[09/19/2023 09:26:14 INFO 140460310353728] Epoch[23] Validation-accuracy=0.839286
[09/19/2023 09:26:18 INFO 140460310353728] Epoch[24] Batch [20]#011Speed: 183.109 samples/sec#011accuracy=0.812500
[09/19/2023 09:26:20 INFO 140460310353728] Epoch[24] Train-accuracy=0.827621
[09/19/2023 09:26:20 INFO 140460310353728] Epoch[24] Time cost=5.175
[09/19/2023 09:26:20 INFO 140460310353728] Epoch[24] Validation-accuracy=0.848958
[09/19/2023 09:26:24 INFO 140460310353728] Epoch[25] Batch [20]#011Speed: 186.768 samples/sec#011accuracy=0.819940
[09/19/2023 09:26:26 INFO 140460310353728] Epoch[25] Train-accuracy=0.826613
[09/19/2023 09:26:26 INFO 140460310353728] Epoch[25] Time cost=5.104
[09/19/2023 09:26:26 INFO 140460310353728] Epoch[25] Validation-accuracy=0.880208
[09/19/2023 09:26:27 INFO 140460310353728] Storing the best model with validation accuracy: 0.880208
[09/19/2023 09:26:27 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0026.params"
[09/19/2023 09:26:30 INFO 140460310353728] Epoch[26] Batch [20]#011Speed: 186.363 samples/sec#011accuracy=0.839286
[09/19/2023 09:26:32 INFO 140460310353728] Epoch[26] Train-accuracy=0.841734
[09/19/2023 09:26:32 INFO 140460310353728] Epoch[26] Time cost=5.101
[09/19/2023 09:26:33 INFO 140460310353728] Epoch[26] Validation-accuracy=0.864583
[09/19/2023 09:26:36 INFO 140460310353728] Epoch[27] Batch [20]#011Speed: 185.196 samples/sec#011accuracy=0.851190
[09/19/2023 09:26:38 INFO 140460310353728] Epoch[27] Train-accuracy=0.855847
[09/19/2023 09:26:38 INFO 140460310353728] Epoch[27] Time cost=5.148
[09/19/2023 09:26:39 INFO 140460310353728] Epoch[27] Validation-accuracy=0.830357
[09/19/2023 09:26:43 INFO 140460310353728] Epoch[28] Batch [20]#011Speed: 183.663 samples/sec#011accuracy=0.866071
[09/19/2023 09:26:44 INFO 140460310353728] Epoch[28] Train-accuracy=0.860887
[09/19/2023 09:26:44 INFO 140460310353728] Epoch[28] Time cost=5.172
[09/19/2023 09:26:45 INFO 140460310353728] Epoch[28] Validation-accuracy=0.864583
[09/19/2023 09:26:49 INFO 140460310353728] Epoch[29] Batch [20]#011Speed: 186.266 samples/sec#011accuracy=0.842262
[09/19/2023 09:26:51 INFO 140460310353728] Epoch[29] Train-accuracy=0.851815
[09/19/2023 09:26:51 INFO 140460310353728] Epoch[29] Time cost=5.121
[09/19/2023 09:26:51 INFO 140460310353728] Epoch[29] Validation-accuracy=0.901042
[09/19/2023 09:26:51 INFO 140460310353728] Storing the best model with validation accuracy: 0.901042
[09/19/2023 09:26:52 INFO 140460310353728] Saved checkpoint to "/opt/ml/model/image-classification-0030.params"

2023-09-19 09:27:23 Completed - Training job completed
Training seconds: 478
Billable seconds: 478

If all goes well, you'll end up with a model topping out above .8 validation accuracy. With only 1000 training samples in the CIFAR dataset, that's pretty good. We could definitely pursue data augmentation & gathering more samples to help us improve further, but for now let's proceed to deploy our model.

Getting ready to deploy¶

To begin with, let's configure Model Monitor to track our deployment. We'll define a DataCaptureConfig below:

In [54]:
from sagemaker.model_monitor import DataCaptureConfig

data_capture_config = DataCaptureConfig(
    ## TODO: Set config options
    destination_s3_uri=f"s3://{bucket}/data_capture", sampling_percentage=100,enable_capture=True
)

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml

Note the destination_s3_uri parameter: At the end of the project, we can explore the data_capture directory in S3 to find crucial data about the inputs and outputs Model Monitor has observed on our model endpoint over time.

With that done, deploy your model on a single ml.m5.xlarge instance with the data capture config attached:

In [56]:
deployment = img_classifier_model.deploy(initial_instance_count=1,
    instance_type="ml.m5.xlarge", 
    data_capture_config=data_capture_config
    ## TODO: fill in deployment options
    )

endpoint = deployment.endpoint_name
print(endpoint)

INFO:sagemaker:Creating model with name: img-classification-job-2023-09-19-09-31-15-559
INFO:sagemaker:Creating endpoint-config with name img-classification-job-2023-09-19-09-31-15-559
INFO:sagemaker:Creating endpoint with name img-classification-job-2023-09-19-09-31-15-559

----------!img-classification-job-2023-09-19-09-31-15-559

Note the endpoint name for later as well.

Next, instantiate a Predictor:

In [58]:
predictor = sagemaker.predictor.Predictor(endpoint) ## TODO: fill in

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml

In the code snippet below we are going to prepare one of your saved images for prediction. Use the predictor to process the payload.

In [59]:
from sagemaker.serializers import IdentitySerializer
import base64

predictor.serializer = IdentitySerializer("image/png")
with open("./test/bicycle_s_001789.png", "rb") as f:
    payload = f.read()

    
inference = predictor.predict(payload) ## TODO: Process the payload with your predictor

Your inference object is an array of two values, the predicted probability value for each of your classes (bicycle and motorcycle respectively.) So, for example, a value of b'[0.91, 0.09]' indicates the probability of being a bike is 91% and being a motorcycle is 9%.

In [60]:
print(inference)

b'[0.6695230007171631, 0.3304769694805145]'

Draft Lambdas and Step Function Workflow¶

Your operations team uses Step Functions to orchestrate serverless workflows. One of the nice things about Step Functions is that workflows can call other workflows, so the team can easily plug your workflow into the broader production architecture for Scones Unlimited.

In this next stage you're going to write and deploy three Lambda functions, and then use the Step Functions visual editor to chain them together! Our functions are going to work with a simple data object:

{
    "inferences": [], # Output of predictor.predict
    "s3_key": "", # Source data S3 key
    "s3_bucket": "", # Source data S3 bucket
    "image_data": ""  # base64 encoded string containing the image data
}

A good test object that you can use for Lambda tests and Step Function executions, throughout the next section, might look like this:

{
  "image_data": "",
  "s3_bucket": MY_BUCKET_NAME, # Fill in with your bucket
  "s3_key": "test/bicycle_s_000513.png"
}

Using these fields, your functions can read and write the necessary data to execute your workflow. Let's start with the first function. Your first Lambda function will copy an object from S3, base64 encode it, and then return it to the step function as image_data in an event.

Go to the Lambda dashboard and create a new Lambda function with a descriptive name like "serializeImageData" and select thr 'Python 3.8' runtime. Add the same permissions as the SageMaker role you created earlier. (Reminder: you do this in the Configuration tab under "Permissions"). Once you're ready, use the starter code below to craft your Lambda handler:

import json
import boto3
import base64

s3 = boto3.client('s3')

def lambda_handler(event, context):
    """A function to serialize target data from S3"""

    # Get the s3 address from the Step Function event input
    key = ## TODO: fill in
    bucket = ## TODO: fill in

    # Download the data from s3 to /tmp/image.png
    ## TODO: fill in

    # We read the data from a file
    with open("/tmp/image.png", "rb") as f:
        image_data = base64.b64encode(f.read())

    # Pass the data back to the Step Function
    print("Event:", event.keys())
    return {
        'statusCode': 200,
        'body': {
            "image_data": image_data,
            "s3_bucket": bucket,
            "s3_key": key,
            "inferences": []
        }
    }

The next function is responsible for the classification part - we're going to take the image output from the previous function, decode it, and then pass inferences back to the the Step Function.

Because this Lambda will have runtime dependencies (i.e. the SageMaker SDK) you'll need to package them in your function. Key reading: https://docs.aws.amazon.com/lambda/latest/dg/python-package-create.html#python-package-create-with-dependency

Create a new Lambda function with the same rights and a descriptive name, then fill in the starter code below for your classifier Lambda.

import json
import sagemaker
import base64
from sagemaker.serializers import IdentitySerializer

# Fill this in with the name of your deployed model
ENDPOINT = ## TODO: fill in

def lambda_handler(event, context):

    # Decode the image data
    image = base64.b64decode(## TODO: fill in)

    # Instantiate a Predictor
    predictor = ## TODO: fill in

    # For this model the IdentitySerializer needs to be "image/png"
    predictor.serializer = IdentitySerializer("image/png")

    # Make a prediction:
    inferences = ## TODO: fill in

    # We return the data back to the Step Function    
    event["inferences"] = inferences.decode('utf-8')
    return {
        'statusCode': 200,
        'body': json.dumps(event)
    }

Finally, we need to filter low-confidence inferences. Define a threshold between 1.00 and 0.000 for your model: what is reasonble for you? If the model predicts at .70 for it's highest confidence label, do we want to pass that inference along to downstream systems? Make one last Lambda function and tee up the same permissions:

import json


THRESHOLD = .93


def lambda_handler(event, context):

    # Grab the inferences from the event
    inferences = ## TODO: fill in

    # Check if any values in our inferences are above THRESHOLD
    meets_threshold = ## TODO: fill in

    # If our threshold is met, pass our data back out of the
    # Step Function, else, end the Step Function with an error
    if meets_threshold:
        pass
    else:
        raise("THRESHOLD_CONFIDENCE_NOT_MET")

    return {
        'statusCode': 200,
        'body': json.dumps(event)
    }

Once you have tested the lambda functions, save the code for each lambda function in a python script called 'lambda.py'.

With your lambdas in place, you can use the Step Functions visual editor to construct a workflow that chains them together. In the Step Functions console you'll have the option to author a Standard step function Visually.

When the visual editor opens, you'll have many options to add transitions in your workflow. We're going to keep it simple and have just one: to invoke Lambda functions. Add three of them chained together. For each one, you'll be able to select the Lambda functions you just created in the proper order, filter inputs and outputs, and give them descriptive names.

Make sure that you:

  1. Are properly filtering the inputs and outputs of your invokations (e.g. $.body)
  2. Take care to remove the error handling from the last function - it's supposed to "fail loudly" for your operations colleagues!

Take a screenshot of your working step function in action and export the step function as JSON for your submission package.

Great! Now you can use the files in ./test as test files for our workflow. Depending on our threshold, our workflow should reliably pass predictions about images from ./test on to downstream systems, while erroring out for inferences below our confidence threshold!

Testing and Evaluation¶

Do several step function invokations using data from the ./test folder. This process should give you confidence that the workflow both succeeds AND fails as expected. In addition, SageMaker Model Monitor will generate recordings of your data and inferences which we can visualize.

Here's a function that can help you generate test inputs for your invokations:

In [62]:
import random
import boto3
import json


def generate_test_case():
    # Setup s3 in boto3
    s3 = boto3.resource('s3')
    
    # Randomly pick from sfn or test folders in our bucket
    objects = s3.Bucket(bucket).objects.filter(Prefix="test")
    
    # Grab any random object key from that folder!
    obj = random.choice([x.key for x in objects])
    
    return json.dumps({
        "image_data": "",
        "s3_bucket": bucket,
        "s3_key": obj
    })
generate_test_case()
Out[62]:
'{"image_data": "", "s3_bucket": "sconesmodel", "s3_key": "test/test_image_1555.png"}'

In the Step Function dashboard for your new function, you can create new executions and copy in the generated test cases. Do several executions so that you can generate data you can evaluate and visualize.

Once you've done several executions, let's visualize the record of our inferences. Pull in the JSONLines data from your inferences like so:

In [64]:
from sagemaker.s3 import S3Downloader

# In S3 your data will be saved to a datetime-aware path
# Find a path related to a datetime you're interested in
data_path ="s3://sconesmodel/data_capture/img-classification-job-2023-09-19-09-31-15-559/AllTraffic/2023/09/19/09/38-12-480-f9170711-9b4e-420c-a6cb-9355ecfad378.jsonl" 
## TODO: fill in the path to your captured data

S3Downloader.download(data_path, "captured_data")

# Feel free to repeat this multiple times and pull in more data

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml
Out[64]:
['captured_data/38-12-480-f9170711-9b4e-420c-a6cb-9355ecfad378.jsonl']

The data are in JSONLines format, where multiple valid JSON objects are stacked on top of eachother in a single jsonl file. We'll import an open-source library, jsonlines that was purpose built for parsing this format.

In [65]:
!pip install jsonlines
import jsonlines

Collecting jsonlines
  Obtaining dependency information for jsonlines from https://files.pythonhosted.org/packages/f8/62/d9ba6323b9202dd2fe166beab8a86d29465c41a0288cbe229fac60c1ab8d/jsonlines-4.0.0-py3-none-any.whl.metadata
  Downloading jsonlines-4.0.0-py3-none-any.whl.metadata (1.6 kB)
Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.10/site-packages (from jsonlines) (23.1.0)
Downloading jsonlines-4.0.0-py3-none-any.whl (8.7 kB)
Installing collected packages: jsonlines
Successfully installed jsonlines-4.0.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

Now we can extract the data from each of the source files:

In [66]:
import os

# List the file names we downloaded
file_handles = os.listdir("./captured_data")

# Dump all the data into an array
json_data = []
for jsonl in file_handles:
    with jsonlines.open(f"./captured_data/{jsonl}") as f:
        json_data.append(f.read())

The data should now be a list of dictionaries, with significant nesting. We'll give you an example of some code that grabs data out of the objects and visualizes it:

In [67]:
# Define how we'll get our data
def simple_getter(obj):
    inferences = obj["captureData"]["endpointOutput"]["data"]
    timestamp = obj["eventMetadata"]["inferenceTime"]
    return json.loads(inferences), timestamp

simple_getter(json_data[0])
Out[67]:
([0.6695230007171631, 0.3304769694805145], '2023-09-19T09:38:12Z')

Finally, here's an example of a visualization you can build with this data. In this last part, you will take some time and build your own - the captured data has the input images, the resulting inferences, and the timestamps.

In [68]:
# Populate the data for the x and y axis
x = []
y = []
for obj in json_data:
    inference, timestamp = simple_getter(obj)
    
    y.append(max(inference))
    x.append(timestamp)

# Todo: here is an visualization example, take some time to build another visual that helps monitor the result
# Plot the data
plt.scatter(x, y, c=['r' if k<.94 else 'b' for k in y ])
plt.axhline(y=0.94, color='g', linestyle='--')
plt.ylim(bottom=.88)

# Add labels
plt.ylabel("Confidence")
plt.suptitle("Observed Recent Inferences", size=14)
plt.title("Pictured with confidence threshold for production use", size=10)

# Give it some pizzaz!
plt.style.use("Solarize_Light2")
plt.gcf().autofmt_xdate()

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
In [69]:
# Populate the data for the x and y axis
%matplotlib inline
THRESHOLD = 0.70
x = []
y = []
for obj in json_data:
    inference, timestamp = simple_getter(obj)
    
    y.append(max(inference))
    x.append(timestamp)

# Todo: here is an visualization example, take some time to build another visual that helps monitor the result
# Plot the data

plt.figure(figsize=(8,6))
plt.scatter(x, y, c=['r' if k<THRESHOLD else 'b' for k in y ])
plt.axhline(y=THRESHOLD, color='g', linestyle='--')
plt.ylim(bottom=0,top=1.02)

# Add labels
plt.ylabel("Confidence")
plt.xlabel("Timestamps")
plt.suptitle("Observed Recent Inferences", size=14, y=0.97, fontweight='bold')
plt.title("Pictured with confidence threshold for production use", size=12)

# Give it some pizzaz!
plt.style.use("Solarize_Light2")
plt.gcf().autofmt_xdate()
plt.show()

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
In [71]:
def my_simple_getter(obj):
    inferences = obj["captureData"]["endpointOutput"]["data"]
    timestamp = obj["eventMetadata"]["inferenceTime"]
    images = obj["captureData"]["endpointInput"]["data"]
    return json.loads(inferences), timestamp, images

# Example: first object retrieved
my_simple_getter(json_data[0])

%matplotlib inline
import io
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from PIL import Image

x = []
y = []
for idx, obj in enumerate(json_data):
    inference, timestamp, image = my_simple_getter(obj)
    img = base64.b64decode(image)     # decode string using base64
    img = io.BytesIO(img) 
    img = Image.open(img)             # Open image
    
    # Plotting subplots to display Images along with their performances
    f = plt.figure(figsize=(12, 6))
    # f.suptitle("Observed Recent Inferences", size=16)
    f.suptitle("Observed Recent Inference", size=15, y=1.04, fontweight='bold')
    
    
    # Plot Test Image
    ax1 = f.add_subplot(121)
    ax1.set_title(f"Test Image {idx+1}", size=13, y=1.11)
    ax1.imshow(img, interpolation='nearest')
    
    
    # Plot Inference and Time Data
    ax2 = f.add_subplot(122)
    ax2.scatter(timestamp, max(inference), c=['r' if max(inference)<THRESHOLD else 'b'])
    ax2.axhline(y=THRESHOLD, color='g', linestyle='--')
    ax2.set_ylim(bottom=0,top=1.02)
    # Add labels
    ax2.set_ylabel("Confidence")
    ax2.set_title("Pictured with confidence threshold\n for production use", size=13, y=1.02)
    
    plt.show()

INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.

Todo: build your own visualization¶

Congratulations!¶

You've reached the end of the project. In this project you created an event-drivent ML workflow that can be incorporated into the Scones Unlimited production architecture. You used the SageMaker Estimator API to deploy your SageMaker Model and Endpoint, and you used AWS Lambda and Step Functions to orchestrate your ML workflow. Using SageMaker Model Monitor, you instrumented and observed your Endpoint, and at the end of the project you built a visualization to help stakeholders understand the performance of the Endpoint over time. If you're up for it, you can even go further with these stretch goals:

  • Extend your workflow to incorporate more classes: the CIFAR dataset includes other vehicles that Scones Unlimited can identify with this model.
  • Modify your event driven workflow: can you rewrite your Lambda functions so that the workflow can process multiple image inputs in parallel? Can the Step Function "fan out" to accomodate this new workflow?
  • Consider the test data generator we provided for you. Can we use it to create a "dummy data" generator, to simulate a continuous stream of input data? Or a big paralell load of data?
  • What if we want to get notified every time our step function errors out? Can we use the Step Functions visual editor in conjunction with a service like SNS to accomplish this? Try it out!